We focus on obj #1: are urban interventions tend to be located in low SES neighborhoods. Here, the urban interventions considered are bike lanes and canopy/tree coverage and low SES ~ high Pampalon deprivation index. In a second step, we will look at the variations of UI and SES and their association.
Data extraction and pre-analyses for the paper looking at BEI and equity. BEI comprise bike lanes and canopy changes while equity is measured through Pampalon deprivation index. (Partially adapted from original work on BEI bike lanes – see bike_lane_stats.R and ReadMe.md.)
Paper is available here.
General processing steps:
In a second phase, these BEI changes will be linked to Pampalon index for 2016 (and 2011 ?)
UPDATE 2021-12-02 Following discussion with @Yan, add normalized bike line changes:
UPDATE 2021-12-08 Following discussion with @Yan
UPDATE 2022-01-14 Following discussion with @Ruben and @Yan
Year variable as continuous instead of category in LMEwSCOREMAT matches theoretical range of x-axis in graph (-.2 >> .2), might ponder the magnitude of the observed trendWe use data categorized by Philippe Apparicio’s team who manually identified bike lanes for each census year since 1991. For this study, we limit ourselves to 2016 and 2011 census years.
On top of the original CT boundaries, three levels of buffer have been applied to the CT – 250m, 500m & 750m. Then the same series of processing steps (see above) have been applied to the buffers.
# Bike lanes, from Ph. Apparicio
reseau <- st_read(dsn="data/ReseauCyclableFinal.gdb", layer = "Reseau") # Already in NAD83 / MTM zone 8
## Reading layer `Reseau' from data source
## `/Users/benoit/WORKSPACE/gentrification_BEI_equity/data/ReseauCyclableFinal.gdb'
## using driver `OpenFileGDB'
## Simple feature collection with 82166 features and 72 fields
## Geometry type: GEOMETRY
## Dimension: XYZ, XYZM
## Bounding box: xmin: 266985.5 ymin: 5029251 xmax: 320986.1 ymax: 5062652
## z_range: zmin: 0 zmax: 43
## m_range: mmin: 0 mmax: 43
## Projected CRS: NAD83 / MTM zone 8
bike_lane <- reseau %>%
filter(An2016 == 1 | An2011 == 1) %>%
select(IdRte, ClsRte, Zone, starts_with("An"), starts_with("Typo_")) %>%
st_cast("MULTILINESTRING") # Get rid of a few MULTICURVE geometries
# CT boundaries for Montreal
CT16 <- get_census(dataset='CA16', regions=list(CMA='24462'), level='CT', geo_format = "sf") %>%
filter(Type == "CT") %>%
mutate(interact_aoi = CD_UID %in% c(2466, 2465, 2458)) %>% # Flag Montréal island, Laval and the South shore (Longueuil, St-Lambert, Brossard)
st_transform(st_crs(bike_lane))
## Reading geo data from local cache.
CT11 <- get_census(dataset='CA11', regions=list(CMA='24462'), level='CT', geo_format = "sf") %>%
filter(Type == "CT") %>%
st_transform(st_crs(bike_lane))
## Reading geo data from local cache.
compute_bikelane_by_area <- function(sf_areas, year_fld, typo_fld) {
# Compute length of bike lanes within each area.
# --
# Parameters:
# - sf_areas: sf class object defining the areas of interest, must have a GeoUID field
# - year_fld: field name specifying the year of interest, e.g. An2016
# - typo_fld: field name specifying the typology for the year of interest, e.g. Typo_2016
year_fld <- enquo(year_fld)
typo_fld <- enquo(typo_fld)
# Compute intersection of bike lanes with areas
bk <- bike_lane %>%
filter(!!year_fld == 1) %>%
st_intersection(sf_areas) %>%
mutate(bike_lane_length = st_length(.)) %>%
as.data.frame() %>%
group_by(GeoUID, !!typo_fld) %>%
summarise(bike_lane_length = sum(bike_lane_length)) %>%
ungroup() %>%
pivot_wider(names_from = !!typo_fld, names_prefix = "Bike_class", names_sort = TRUE,
values_from = bike_lane_length, values_fill = units::set_units(0, m)) %>%
mutate(Bike_lane_total = units::set_units(rowSums(select(., starts_with("Bike_class"))), m))
# Merge back into original sf_areas
bk <- sf_areas %>%
left_join(bk)
# Replace NA by 0, which occur in Bike_class length
bk[is.na(bk)] <- 0
return(bk)
}
compute_streetlength_by_area <- function(sf_areas) {
# Compute length of streets within each area.
# --
# Parameters:
# - sf_areas: sf class object defining the areas of interest, must have a GeoUID field
# Compute intersection of streets with areas
bk <- reseau %>%
st_cast("MULTILINESTRING") %>% # Get rid of a few MULTICURVE geometries
st_intersection(sf_areas) %>%
mutate(street_length = st_length(.)) %>%
as.data.frame() %>%
group_by(GeoUID) %>%
summarise(street_length = sum(street_length)) %>%
ungroup()
# Merge back into original sf_areas
bk <- sf_areas %>%
mutate(shape_area_km2 = units::set_units(st_area(.), 'km^2')) %>%
left_join(bk)
# Replace NA by 0
bk[is.na(bk)] <- 0
return(bk)
}
# Compute year 2016 and year 2011 bike lanes within 2016 CTs
# NB: contrary to the original work, we keep the same area of reference, i.e. 2016
bike_lane_by_CT16 <- compute_bikelane_by_area(CT16, An2016, Typo_2016)
bike_lane_by_CT11 <- compute_bikelane_by_area(CT16, An2011, Typo_2011)
bike_lane_by_CT06 <- compute_bikelane_by_area(CT16, An2006, Typo_2006)
# Compute buffers, with 3 radii and for each census year
radii <- c(250, 500, 750)
buf_CT16 <- lapply(radii, st_buffer, x=CT16)
names(buf_CT16) <- lapply(radii, function(b) {paste0("buf", b)})
# Compute bike length for each buffer/census year
buf_CT16_w_bike_length <- lapply(buf_CT16, compute_bikelane_by_area, year_fld=An2016, typo_fld=Typo_2016)
names(buf_CT16_w_bike_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT16_w_bike_length$original <- bike_lane_by_CT16
buf_CT11_w_bike_length <- lapply(buf_CT16, compute_bikelane_by_area, year_fld=An2011, typo_fld=Typo_2011)
names(buf_CT11_w_bike_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT11_w_bike_length$original <- bike_lane_by_CT11
buf_CT06_w_bike_length <- lapply(buf_CT16, compute_bikelane_by_area, year_fld=An2006, typo_fld=Typo_2006)
names(buf_CT06_w_bike_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT06_w_bike_length$original <- bike_lane_by_CT06
# Compute total street length within CT/buffer
street_length_by_CT16 <- compute_streetlength_by_area(CT16)
buf_CT16_street_length <- lapply(buf_CT16, compute_streetlength_by_area)
names(buf_CT16_street_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT16_street_length$original <- street_length_by_CT16
# Reorganize data to have all data in one dataframe
bike_lane_changes <- CT16 %>%
left_join(select(as.data.frame(buf_CT16_street_length$original), GeoUID, street_length, shape_area_km2), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT16_street_length$buf250), GeoUID, street_length, shape_area_km2), by="GeoUID", suffix=c(".ct", ".b250")) %>%
left_join(select(as.data.frame(buf_CT16_street_length$buf500), GeoUID, street_length, shape_area_km2), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT16_street_length$buf750), GeoUID, street_length, shape_area_km2), by="GeoUID", suffix=c(".b500", ".b750")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$original), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$original), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016ct", ".2011ct")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$buf250), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$buf250), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016b250", ".2011b250")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$buf500), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$buf500), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016b500", ".2011b500")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$buf750), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$buf750), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016b750", ".2011b750")) %>%
left_join(select(as.data.frame(buf_CT06_w_bike_length$original), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT06_w_bike_length$buf250), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2006ct", ".2006b250")) %>%
left_join(select(as.data.frame(buf_CT06_w_bike_length$buf500), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT06_w_bike_length$buf750), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2006b500", ".2006b750"))
# Compute ratio of bike lane vs street length
bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane.by.street.2011ct = 100 * Bike_lane_total.2011ct / street_length.ct,
Bike_lane.by.street.2011b250 = 100 * Bike_lane_total.2011b250 / street_length.b250,
Bike_lane.by.street.2011b500 = 100 * Bike_lane_total.2011b500 / street_length.b500,
Bike_lane.by.street.2011b750 = 100 * Bike_lane_total.2011b750 / street_length.b750,
Bike_lane.by.street.2016ct = 100 * Bike_lane_total.2016ct / street_length.ct,
Bike_lane.by.street.2016b250 = 100 * Bike_lane_total.2016b250 / street_length.b250,
Bike_lane.by.street.2016b500 = 100 * Bike_lane_total.2016b500 / street_length.b500,
Bike_lane.by.street.2016b750 = 100 * Bike_lane_total.2016b750 / street_length.b750,
Bike_lane.by.street.2006ct = 100 * Bike_lane_total.2006ct / street_length.ct,
Bike_lane.by.street.2006b250 = 100 * Bike_lane_total.2006b250 / street_length.b250,
Bike_lane.by.street.2006b500 = 100 * Bike_lane_total.2006b500 / street_length.b500,
Bike_lane.by.street.2006b750 = 100 * Bike_lane_total.2006b750 / street_length.b750)
# Compute change between 2011 and 2016 (only for total bike lane length)
bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane_diff.2011.2016ct = Bike_lane_total.2016ct - Bike_lane_total.2011ct,
Bike_lane_diff.2011.2016b250 = Bike_lane_total.2016b250 - Bike_lane_total.2011b250,
Bike_lane_diff.2011.2016b500 = Bike_lane_total.2016b500 - Bike_lane_total.2011b500,
Bike_lane_diff.2011.2016b750 = Bike_lane_total.2016b750 - Bike_lane_total.2011b750)
# Normalize bike lane change by (i) street length and (ii) area
bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane_diff.by.street.2011.2016ct = 100 * Bike_lane_diff.2011.2016ct / street_length.ct,
Bike_lane_diff.by.street.2011.2016b250 = 100 * Bike_lane_diff.2011.2016b250 / street_length.b250,
Bike_lane_diff.by.street.2011.2016b500 = 100 * Bike_lane_diff.2011.2016b500 / street_length.b500,
Bike_lane_diff.by.street.2011.2016b750 = 100 * Bike_lane_diff.2011.2016b750 / street_length.b750,
Bike_lane_diff.by.area.2011.2016ct = Bike_lane_diff.2011.2016ct / shape_area_km2.ct,
Bike_lane_diff.by.area.2011.2016b250 = Bike_lane_diff.2011.2016b250 / shape_area_km2.b250,
Bike_lane_diff.by.area.2011.2016b500 = Bike_lane_diff.2011.2016b500 / shape_area_km2.b500,
Bike_lane_diff.by.area.2011.2016b750 = Bike_lane_diff.2011.2016b750 / shape_area_km2.b750)
# Save results
st_write(bike_lane_changes, dsn = "data/bike_length_changes.gpkg", delete_layer = TRUE)
## Deleting layer `bike_length_changes' using driver `GPKG'
## Writing layer `bike_length_changes' to data source
## `data/bike_length_changes.gpkg' using driver `GPKG'
## Writing 970 features with 140 fields and geometry type Multi Polygon.
# Clean up
rm(buf_CT16)
rm(bike_lane_by_CT11, bike_lane_by_CT16, bike_lane_by_CT06)
rm(buf_CT11_w_bike_length, buf_CT16_w_bike_length, buf_CT06_w_bike_length)
rm(street_length_by_CT16, buf_CT16_street_length, buf_CT06_street_length)
Check output for one specific dataset (Census tracts 2016, no buffer)
Bike lane length in 2016 within CTs, measured in meters
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_total.2016ct)), lwd=0) +
scale_fill_continuous(name = "Total length (m)")+
labs(title = "Length of bike lanes within 2016 CTs", subtitle = "(INTERACT study area || for control only)")
Absolute bike lane length change between 2011 and 2016, in meters
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Length (m)")+
labs(title = "Changes in bike lane between 2011 and 2016", subtitle = "(INTERACT study area || CT level || for control only)")
Relative bike lane length change between 2011 and 2016, normalized by street length within CT in 2016, expressed in %
\[Variation = \frac{Bike Lane_{2016}}{Street Length_{2016}} - \frac{Bike Lane_{2011}}{Street Length_{2016}}\]
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.by.street.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Variation (%)")+
labs(title = "Changes in bike lane between 2011 and 2016, normalized by street", subtitle = "(INTERACT study area || CT level || for control only)")
Relative bike lane length change between 2011 and 2016, normalized by CT area, expressed in \(\frac{km}{km^{2}}\)
\[Ratio = \frac{Bike Lane_{2016}}{CT Area_{2016}} - \frac{Bike Lane_{2011}}{CT Area_{2016}}\]
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.by.area.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Ratio (1/km)")+
labs(title = "Changes in bike lane between 2011 and 2016, normalized by area", subtitle = "(INTERACT study area || CT level || for control only)")
After some discussion with Yan, we envision using a relative bike lane length ratio change between 2011 and 2016, normalized by the ratio in 2011, expressed in %
\[Variation = \frac{\frac{Bike Lane_{2016}}{Street Length_{2016}} - \frac{Bike Lane_{2011}}{Street Length_{2016}}}{\frac{Bike Lane_{2011}}{Street Length_{2016}}} = \frac{Bike Lane_{2016} - Bike Lane_{2011}}{Bike Lane_{2011}}\]
Problem: all CT with no bike lane in 2011 get a missing data, contrary to the original metric, which measured the absolute variation of the ratio of bike lane to street length.
.bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane_diff.by.street.relative.2011.2016ct = Bike_lane_diff.by.street.2011.2016ct / (Bike_lane_total.2011ct / street_length.ct))
ggplot() +
geom_sf(data=filter(.bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.by.street.relative.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Ratio")+
labs(title = "Changes in bike lane ratio between 2011 and 2016, normalized by ratio in 2011", subtitle = "(INTERACT study area || CT level || for control only)")
# CT level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016ct)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | CT level")
summary(bike_lane_changes$Bike_lane_diff.2011.2016ct)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1377.7 0.0 0.0 262.1 189.7 14463.8
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016ct))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 252 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016ct)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -100.000 0.000 0.000 3.665 4.496 100.000 252
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016ct))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016ct)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.1474 0.0000 0.0000 0.4237 0.1764 8.9852
ggplot(.bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.relative.2011.2016ct))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by ratio in 2011")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 461 rows containing non-finite values (stat_bin).
summary(.bike_lane_changes$Bike_lane_diff.by.street.relative.2011.2016ct)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -100.0000 0.0000 0.2199 Inf 102.7796 Inf 367
# buf250 level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016b250)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | buf 250m")
summary(bike_lane_changes$Bike_lane_diff.2011.2016b250)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3452.5 0.0 0.0 646.7 933.8 20786.8
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016b250))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 233 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016b250)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -38.5802 0.0000 0.8538 3.3168 4.9043 56.6588 233
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016b250))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016b250)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.4622 0.0000 0.0000 0.4001 0.4306 6.2156
# buf500 level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016b500)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | buf 500m")
summary(bike_lane_changes$Bike_lane_diff.2011.2016b250)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3452.5 0.0 0.0 646.7 933.8 20786.8
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016b500))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 229 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016b500)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -26.891 0.000 1.385 3.298 5.323 22.121 229
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016b500))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016b500)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.94397 0.00000 0.03393 0.38745 0.49370 4.00417
# buf750 level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016b750)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | buf 750m")
summary(bike_lane_changes$Bike_lane_diff.2011.2016b750)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3697.5 0.0 578.6 1842.2 2928.6 29325.1
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016b750))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 222 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016b750)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.7579 0.1418 1.7793 3.2961 5.2054 22.5448 222
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016b750))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016b750)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.59760 0.00000 0.07793 0.38166 0.53774 3.95463
Canopy changes is based on data produced by CMM, using multispectral aerial imagery and lidar. In order to sync the observations with the census years, we focus on 2011 and 2019 with one extra observation point in 2015.
The processing steps are similar to the ones for the bike lanes:
# Codes du raster "espace vert"
# 0. No data (hors CMM)
# 1. NDVI < 0,3 et MNH < 3,0m = Minéral bas (route, stationnement, etc.)
# 2. NDVI < 0,3 et MNH ≥ 3,0m = Minéral haut (constructions)
# 3. NDVI ≥ 0,3 et MNH < 3,0m = Végétal bas (culture, gazon, etc.)
# 4. NDVI ≥ 0,3 et MNH ≥ 3,0m = Végétal haut (canopée)
# 5. Aquatique
# Load rasters into pg database for further processing
system('psql -d gentrif_bei -c "CREATE EXTENSION IF NOT EXISTS postgis"')
system('psql -d gentrif_bei -c "CREATE EXTENSION IF NOT EXISTS postgis_raster"')
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2019') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2019/*.tif -F -t 1000x1000 canopee2019 | psql -d gentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2019' already imported") }
## PG Raster 'canopee2019' already imported
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2017') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2017/*.tif -F -t 1000x1000 canopee2017 | psql -d gentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2017' already imported") }
## PG Raster 'canopee2017' already imported
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2015') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2015/*.tif -F -t 1000x1000 canopee2015 | psql -d gentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2015' already imported") }
## PG Raster 'canopee2015' already imported
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2011') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2011/*.tif -F -t 1000x1000 canopee2011 | psql -d gentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2011' already imported") }
## PG Raster 'canopee2011' already imported
# Resample to 10m as the original rasters have a 1m resolution, which is too high to allow for a swift processing
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2019_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=gentrif_bei table=canopee2019 mode=2\" -r mode -tr 10 10 data/canopy/canopee2019_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2019_10m.tif -F -t 100x100 canopee2019_10m | psql -d gentrif_bei")
} else { message("PG Raster 'canopee2019_10m' already imported") }
## PG Raster 'canopee2019_10m' already imported
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2017_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=gentrif_bei table=canopee2017 mode=2\" -r mode -tr 10 10 data/canopy/canopee2017_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2017_10m.tif -F -t 100x100 canopee2017_10m | psql -d gentrif_bei")
} else { message("PG Raster 'canopee2017_10m' already imported") }
## PG Raster 'canopee2017_10m' already imported
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2015_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=gentrif_bei table=canopee2015 mode=2\" -r mode -tr 10 10 data/canopy/canopee2015_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2015_10m.tif -F -t 100x100 canopee2015_10m | psql -d gentrif_bei")
} else { message("PG Raster 'canopee2015_10m' already imported") }
## PG Raster 'canopee2015_10m' already imported
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2011_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=gentrif_bei table=canopee2011 mode=2\" -r mode -tr 10 10 data/canopy/canopee2011_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2011_10m.tif -F -t 100x100 canopee2011_10m | psql -d gentrif_bei")
} else { message("PG Raster 'canopee2011_10m' already imported") }
## PG Raster 'canopee2011_10m' already imported
# Push CT16 to pg
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('ct16') IS NOT NULL;")) == 0) {
CT16 %>%
st_transform(crs = 32188) %>%
st_write(con_bei, "ct16",
layer_options = c("OVERWRITE=yes", "LAUNDER=true", "SPATIAL_INDEX=gist", "GEOMETRY_NAME=geom"))
system("psql -d gentrif_bei -c 'CREATE INDEX ON ct16 USING gist (geometry)'")
} else { message("PG Layer CT16 already imported") }
## PG Layer CT16 already imported
UPDATE 2021-12-08: following discussion with Yan, we decide to focus on extracting canopy years in sync with census years, i.e. 2011 and 2017, and 2015 as an intermediary year. (Previously, we were using the last available year, i.e. 2019), plus keep the option of looking high/low canopy separately
UPDATE 2022-02-09: adding the number of sq.m per inhabitant within census.
WITH cnt19 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2019_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt17 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2017_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt15 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2015_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt11 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2011_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2011_by_pop, 0) END m2_esp_vert_2011_by_pop
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2015_by_pop, 0) END m2_esp_vert_2015_by_pop
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2017_by_pop, 0) END m2_esp_vert_2017_by_pop
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2019_by_pop, 0) END m2_esp_vert_2019_by_pop
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");
WITH ct16 AS (
select "GeoUID", "Population", ST_Buffer(geometry, 250) geometry
from ct16
),
cnt19 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2019_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt17 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2017_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt15 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2015_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt11 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2011_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2011_by_pop, 0) END m2_esp_vert_2011_by_pop
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2015_by_pop, 0) END m2_esp_vert_2015_by_pop
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2017_by_pop, 0) END m2_esp_vert_2017_by_pop
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2019_by_pop, 0) END m2_esp_vert_2019_by_pop
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");
WITH ct16 AS (
select "GeoUID", "Population", ST_Buffer(geometry, 500) geometry
from ct16
),
cnt19 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2019_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt17 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2017_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt15 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2015_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt11 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2011_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2011_by_pop, 0) END m2_esp_vert_2011_by_pop
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2015_by_pop, 0) END m2_esp_vert_2015_by_pop
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2017_by_pop, 0) END m2_esp_vert_2017_by_pop
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2019_by_pop, 0) END m2_esp_vert_2019_by_pop
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");
WITH ct16 AS (
select "GeoUID", "Population", ST_Buffer(geometry, 750) geometry
from ct16
),
cnt19 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2019_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt17 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2017_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt15 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2015_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
),
cnt11 AS (
SELECT "GeoUID", "Population"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID", "Population"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", "Population", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(10.*10. * sum(total) FILTER (WHERE value in (3, 4)) / NULLIF("Population", 0), 1) AS m2_esp_vert_2011_by_pop
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID", "Population"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2011_by_pop, 0) END m2_esp_vert_2011_by_pop
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2015_by_pop, 0) END m2_esp_vert_2015_by_pop
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2017_by_pop, 0) END m2_esp_vert_2017_by_pop
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,CASE WHEN "Population" <= 5 THEN NULL ELSE COALESCE(m2_esp_vert_2019_by_pop, 0) END m2_esp_vert_2019_by_pop
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");
Get it here
pampalon <- read.xlsx("data/Canada2016Pampalon/A-MSDIData_Can2016_eng/1. EquivalenceTableCanada2016_ENG.xlsx", sheet = 2) %>%
mutate(DA = as.character(DA)) %>%
select(DA, SCOREMAT, SCORESOC)
# 2016 DA boundaries for Montreal
DA16 <- get_census(dataset='CA16', regions=list(CMA='24462'), level='DA', geo_format = "sf") %>%
filter(Type == "DA") %>%
st_transform(st_crs(bike_lane))
## Reading geo data from local cache.
pampalon <- DA16 %>%
inner_join(pampalon, by = c("GeoUID" = "DA")) %>%
as.data.frame()
# Get Pampalon 2006
pampalon06 <- read.xlsx("data/Canada2006Pampalon/A-MSDIData_Can2006_eng/1. CorrespondenceTable_Can2006_eng.xlsx", sheet = 2) %>%
mutate(DA = as.character(DA)) %>%
select(DA, DAPOP2006, SCOREMAT, SCORESOC)
# Get LUT DA2006 <-> DA2011 from StatCan
lut_da.1 <- read.csv("data/2011_92-156_DA_AD_txt/2011_92-156_DA_AD.txt", colClasses = "character",
header = FALSE, col.names = c("DAUID2011.ADIDU2011", "DAUID2006.ADIDU2006", "DBUID2011", "DA_rel_flag")) %>%
select(!c(DBUID2011, DA_rel_flag)) %>%
unique()
# Link Pampalon 2011 to LUT and compute weighted mean of scores of Pampalon 2011
# NB: population numbers will diverge from reality when more than one DA is merged into one DA of next census
pampalon06.11 <- pampalon06 %>%
inner_join(lut_da.1, by = c("DA" = "DAUID2006.ADIDU2006")) %>%
group_by(DAUID2011.ADIDU2011) %>%
summarise(pop2006 = sum(DAPOP2006),
SCOREMAT.06 = weighted.mean(SCOREMAT, DAPOP2006, na.rm = TRUE),
SCORESOC.06 = weighted.mean(SCORESOC, DAPOP2006, na.rm = TRUE))
# Get Pampalon 2011
pampalon11 <- read.xlsx("data/Canada2011Pampalon/A-MSDIData_Can2011_eng/1. CorrespondenceTable_Can2011_eng.xlsx", sheet = 2) %>%
mutate(DA = as.character(DA)) %>%
select(DA, DAPOP2011, SCOREMAT, SCORESOC)
# Get LUT DA2011 <-> DA2016 from StatCan
lut_da <- read.csv("data/2016_92-156_DA_AD_csv/2016_92-156_DA_AD.csv", colClasses = "character") %>%
select(!c(DBUID2016.IDIDU2016, DA_rel_flag.AD_ind_rel)) %>%
unique()
# Link Pampalon 2011 to LUT, then to Pampalon 06 and finally compute weighted mean of scores of Pampalon 2011
pampalon11.16 <- pampalon11 %>%
inner_join(lut_da, by = c("DA" = "DAUID2011.ADIDU2011")) %>%
left_join(pampalon06.11, by =c("DA" = "DAUID2011.ADIDU2011")) %>%
group_by(DAUID2016.ADIDU2016) %>%
summarise(pop2011 = sum(DAPOP2011),
SCOREMAT = weighted.mean(SCOREMAT, DAPOP2011, na.rm = TRUE),
SCORESOC = weighted.mean(SCORESOC, DAPOP2011, na.rm = TRUE),
SCOREMAT.06 = weighted.mean(SCOREMAT.06, pop2006, na.rm = TRUE),
SCORESOC.06 = weighted.mean(SCORESOC.06, pop2006, na.rm = TRUE),
pop2006 = sum(pop2006))
# Then link Pampalon 2011 to 2016
pampalon <- pampalon %>%
left_join(pampalon11.16, by = c("GeoUID" = "DAUID2016.ADIDU2016"), suffix = c(".16", ".11"))
# Aggregate at the CT level
pampalon_CT <- pampalon %>%
group_by(CT_UID) %>%
summarise(wSCOREMAT.2016 = weighted.mean(SCOREMAT.16, Population, na.rm = TRUE),
wSCORESOC.2016 = weighted.mean(SCORESOC.16, Population, na.rm = TRUE),
wSCOREMAT.2011 = weighted.mean(SCOREMAT.11, pop2011, na.rm = TRUE),
wSCORESOC.2011 = weighted.mean(SCORESOC.11, pop2011, na.rm = TRUE),
wSCOREMAT.2006 = weighted.mean(SCOREMAT.06, pop2006, na.rm = TRUE),
wSCORESOC.2006 = weighted.mean(SCORESOC.06, pop2006, na.rm = TRUE))
# Clean up
rm(lut_da, lut_da.1, pampalon11.16, pampalon06.11, pampalon11, pampalon06)
# Display map
.pampalon_CT_geom <- CT16 %>%
left_join(pampalon_CT, by = c("GeoUID" = "CT_UID")) %>%
filter(interact_aoi)
.pampalon_data <- bi_class(.pampalon_CT_geom, x = wSCOREMAT.2016, y = wSCORESOC.2016, style = "quantile", dim = 3)
## Warning in classInt::classIntervals(bins_x, n = dim, style = "quantile"): var
## has missing values, omitted in finding classes
## Warning in classInt::classIntervals(bins_y, n = dim, style = "quantile"): var
## has missing values, omitted in finding classes
.map <- ggplot() +
geom_sf(data = .pampalon_data, mapping = aes(fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
bi_scale_fill(pal = "DkBlue", dim = 3) +
labs(title = "Pampalon: material and social deprivation index") +
theme(panel.background = element_rect(fill = "white"),
#axis.ticks = element_blank(),
#axis.text = element_blank(),
panel.grid = element_line(color = "darkgray", size = 0.2))
.legend <- bi_legend(pal = "DkBlue",
dim = 3,
xlab = "Material ",
ylab = "Social ",
size = 8)
ggdraw() +
draw_plot(.map, 0, 0, 1, 1) +
draw_plot(.legend, 0.1, .7, 0.2, 0.2)
Using Ding metric computed on 5 year span.
# Load gentrified CTs, 5 year span (from repo gentrification_metrics)
ding <- list()
ding[["2016"]] <- st_read("data/gentrified_5years.gpkg", "gentrified_ding_16", quiet=TRUE) %>%
filter(cma_uid_16 == "24462") %>%
st_transform(st_crs(bike_lane))
ding[["2011"]] <- st_read("data/gentrified_5years.gpkg", "gentrified_ding_11", quiet=TRUE) %>%
filter(cma_uid_11 == "24462") %>%
st_transform(st_crs(bike_lane))
ding[["2006"]] <- st_read("data/gentrified_5years.gpkg", "gentrified_ding_06", quiet=TRUE) %>%
filter(cma_uid_06 == "24462") %>%
st_transform(st_crs(bike_lane))
.ding_map <- ding[["2016"]] %>%
left_join(select(as.data.frame(CT16), GeoUID, interact_aoi), by = c("ct_uid_16" = "GeoUID")) %>%
filter(interact_aoi)
ggplot(data = .ding_map) +
geom_sf(aes(fill = gentrified_2016_2011, colour=gentrifiable_2011)) +
scale_fill_manual(values = c("gray", "red", "darkgray"), name = "Gentrified in 2016") +
scale_colour_manual(values = c("darkgray", "darkred", "darkgray"), name = "Gentrifiable in 2011") +
labs(title = "Census tract gentrification status in 2016")
Introduced here as a proposition, nothing acted (2022-02-04)
# Visible Minority
# - v_CA16_3954: Total - Visible minority for the population in private households - 25% sample data (Total)
# - v_CA16_3957: Total visible minority population (Total)
# Low income (LIM-AT)
# - v_CA16_2540: Prevalence of low income based on the Low-income measure, after tax (LIM-AT) (%) (Total)
equity_ct16 <- get_census(dataset='CA16', regions=list(CMA='24462'), level='CT', geo_format = "sf",
vectors = c("v_CA16_3954", "v_CA16_3957", "v_CA16_2540")) %>%
filter(Type == "CT") %>%
transmute(CT_UID = GeoUID,
vis_minority_2016 = `v_CA16_3957: Total visible minority population` / `v_CA16_3954: Total - Visible minority for the population in private households - 25% sample data` * 100,
low_income_2016 = `v_CA16_2540: Prevalence of low income based on the Low-income measure, after tax (LIM-AT) (%)`)
## Reading vectors data from local cache.
## Reading geo data from local cache.
# Visible Minority
# - v_CA11N_457: CA 2011 NHS, Total population in private households by visible minority (Total)
# - v_CA11N_460: CA 2011 NHS, Total population in private households by visible minority, Total visible minority population (Total)
# Low income (LIM-AT)
# - v_CA11N_2606: CA 2011 NHS, Prevalence of low income in 2010 based on after-tax low-income measure % (Total)
equity_ct11 <- get_census(dataset='CA11', regions=list(CMA='24462'), level='CT', geo_format = "sf",
vectors = c("v_CA11N_457", "v_CA11N_460", "v_CA11N_2606")) %>%
filter(Type == "CT") %>%
transmute(CT_UID = GeoUID,
vis_minority_2011 = `v_CA11N_460: Total visible minority population` / `v_CA11N_457: Total population in private households by visible minority` * 100,
low_income_2011 = `v_CA11N_2606: Prevalence of low income in 2010 based on after-tax low-income measure %`)
## Reading vectors data from local cache.
## Reading geo data from local cache.
# Visible Minority
# - v_CA06_1302: Total population by visible minority groups
# - v_CA06_1303: Total population by visible minority groups, Total visible minority population
# Low income (LIM-AT)
# - v_TX2006_551: After-tax low income status of tax filers and dependents (census family low income measure, CFLIM-AT) for couple and lone parent families by family composition, 2006 | All family units | Persons in Low Income | % - Total
equity_ct06 <- get_census(dataset='CA06', regions=list(CMA='24462'), level='CT', geo_format = "sf",
vectors = c("v_CA06_1302", "v_CA06_1303", "v_TX2006_551")) %>%
filter(Type == "CT") %>%
transmute(CT_UID = GeoUID,
vis_minority_2006 = `v_CA06_1303: Total visible minority population` / `v_CA06_1302: Total population by visible minority groups - 20% sample data` * 100,
low_income_2006 = `v_TX2006_551: % - Total`)
## Reading vectors data from local cache.
## Reading geo data from local cache.
equity_ct <- st_join(equity_ct16, equity_ct11, left=TRUE, largest=TRUE, suffix=c("", "_2011")) %>% # join on largest overlap, to overcome mismatch in CT UID
st_join(equity_ct06, left=TRUE, largest=TRUE, suffix=c("", "_2006")) %>%
data.frame()
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
# cleanup
rm(equity_ct11, equity_ct16, equity_ct06)
# Display map
.equity_CT_geom <- CT16 %>%
left_join(equity_ct, by = c("GeoUID" = "CT_UID")) %>%
filter(interact_aoi)
.equity_data <- bi_class(.equity_CT_geom, x = vis_minority_2016, y = low_income_2016, style = "quantile", dim = 3)
## Warning in classInt::classIntervals(bins_x, n = dim, style = "quantile"): var
## has missing values, omitted in finding classes
## Warning in classInt::classIntervals(bins_y, n = dim, style = "quantile"): var
## has missing values, omitted in finding classes
.map <- ggplot() +
geom_sf(data = .equity_data, mapping = aes(fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
bi_scale_fill(pal = "Brown", dim = 3) +
labs(title = "Equity metrics: % of visible minority and % of low-income household") +
theme(panel.background = element_rect(fill = "white"),
#axis.ticks = element_blank(),
#axis.text = element_blank(),
panel.grid = element_line(color = "darkgray", size = 0.2))
.legend <- bi_legend(pal = "Brown",
dim = 3,
xlab = "Vis. Minority ",
ylab = "Low-Income ",
size = 8)
ggdraw() +
draw_plot(.map, 0, 0, 1, 1) +
draw_plot(.legend, 0.1, .7, 0.2, 0.2)
All variables + outcome linked at the CT level
.bike_lane_changes <- bike_lane_changes %>%
as.data.frame() %>%
select(GeoUID, ends_with("ct", ignore.case = FALSE), ends_with("b250", ignore.case = FALSE), ends_with("b500", ignore.case = FALSE), ends_with("b750", ignore.case = FALSE)) %>%
select(GeoUID, starts_with("Bike_lane")) # Drop individual category lane length
bei_df <- CT16 %>%
as.data.frame() %>%
transmute(CT_UID = GeoUID,
CD_UID = CD_UID,
CSD_UID = CSD_UID,
interact_aoi = interact_aoi,
Population = Population) %>%
left_join(pampalon_CT, by="CT_UID") %>%
left_join(select(as.data.frame(ding$`2016`), ct_uid_16, starts_with("gentrif")), by=c("CT_UID" = "ct_uid_16")) %>%
left_join(select(as.data.frame(ding$`2011`), ct_uid_11, starts_with("gentrif")), by=c("CT_UID" = "ct_uid_11")) %>%
left_join(select(as.data.frame(ding$`2006`), ct_uid_06, starts_with("gentrif")), by=c("CT_UID" = "ct_uid_06")) %>%
left_join(select(as.data.frame(equity_ct), !c("geometry", "CT_UID_2011", "CT_UID_2006")), by="CT_UID") %>%
left_join(.bike_lane_changes, by=c("CT_UID" = "GeoUID")) %>%
left_join(as.data.frame(esp_vert_ct), by=c("CT_UID" = "GeoUID")) %>%
left_join(as.data.frame(esp_vert_buf250), by=c("CT_UID" = "GeoUID"), suffix=c("ct", "b250")) %>%
left_join(as.data.frame(esp_vert_buf500), by=c("CT_UID" = "GeoUID")) %>%
left_join(as.data.frame(esp_vert_buf750), by=c("CT_UID" = "GeoUID"), suffix=c("b500", "b750"))
head(bei_df)
write.csv(bei_df, "data/_results/bei_equity.csv", na="", row.names = FALSE)
Included variables:
CT_UID: 2016 Census Tract IDCD_UID: 2016 Census DivisionCSD_UID: 2016 Census Subdivisioninteract_aoi: Does CT belong to INTERACT study area?Population: 2016 Population within CTct_area_m2.{ct|b{250|500|750}}: Area of CT or buffer of 250, 500 or 750m radius around CT, in square metersgentrified_2016_2011: Is the CT gentrified in 2016?gentrifiable_2011: Is the CT candidate to gentrification in 2011?gentrified_2011_2006: Is the CT gentrified in 2011gentrifiable_2006: Is the CT candidate to gentrification in 2006gentrified_2006_2001: Is the CT gentrified in 2006gentrifiable_2001: Is the CT candidate to gentrification in 2001wSCOREMAT.2016: Social deprivation index in 2016 (population weighted)wSCORESOC.2016: Material deprivation index in 2016 (population weighted)wSCOREMAT.2011: Social deprivation index in 2011 (population weighted)wSCORESOC.2011: Material deprivation index in 2011 (population weighted)wSCOREMAT.2006: Social deprivation index in 2006 (population weighted)wSCORESOC.2006: Material deprivation index in 2006 (population weighted)vis_minority_2016: % of visible minority in CT 2016low_income_2016: prevalence of low income in CT 2016vis_minority_2011: % of visible minority in CT 2011low_income_2011: prevalence of low income in CT 2011vis_minority_2006: % of visible minority in CT 2006low_income_2006: prevalence of low income in CT 2006Bike_lane_total.{2016|2011}{ct|b{250|500|750}}: total length of bike lanes, in 2016 or 2011, within CT or buffer of 250, 500 or 750m radiusBike_lane.by.street.{2016|2011}{ct|b{250|500|750}}: % of bike lanes compared to streets, in 2016 or 2011, within CT or buffer of 250, 500 or 750m radiusBike_lane_diff.2011.2016{ct|b{250|500|750}}: change in total length of bike lanes between 2011 and 2011, within CT or buffer of 250, 500 or 750m radiusBike_lane_diff.by.street.2011.2016{ct|b{250|500|750}}: change in total length of bike lanes between 2011 and 2011, normalized by street length, within CT or buffer of 250, 500 or 750m radiusBike_lane_diff.by.area.2011.2016{ct|b{250|500|750}}: change in total length of bike lanes between 2011 and 2011, normalized by area, within CT or buffer of 250, 500 or 750m radiuspct_esp_vert_{2011|2015|2019}.{ct|b{250|500|750}}: % of green space in 2011, 2015 or 2019 within CT or buffer of 250, 500 or 750m radiuspct_esp_vert_{low|high}_{2011|2015|2019}.{ct|b{250|500|750}}: same as above, except for grass (low) and tree (high)pct_esp_vert_diff{2011|2015}.{2015|2019}.{ct|b{250|500|750}}: change in % of green space between 2011 and 2015, 2011 and 2019 as well as 2011 and 2019, within CT or buffer of 250, 500 or 750m radiusINTERACT study area ~ Montréal, Laval, Longueuil, Brossard, St-Lambert
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("wSCORE")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name) #, scales = "free")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 86 rows containing non-finite values (stat_bin).
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("vis_minority"), starts_with("low_income")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name) #, scales = "free")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 85 rows containing non-finite values (stat_bin).
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("gentrif")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_bar() +
facet_wrap(~name) #, scales = "free", ncol = 3)
# Testing correlation between numeric variables
corrplot::corrplot(cor(select(bei_df, starts_with("wSCORE"), starts_with("vis_"), starts_with("low_")), use = "complete.obs"), type="upper", order="hclust")
# ANOVA for gentrified variables (see https://stats.stackexchange.com/questions/119835/correlation-between-a-nominal-iv-and-a-continuous-dv-variable)
heplots::etasq(aov(gentrified_2016_2011 ~ wSCOREMAT.2011, data = bei_df))
heplots::etasq(aov(gentrified_2016_2011 ~ low_income_2011, data = bei_df))
heplots::etasq(aov(gentrified_2016_2011 ~ vis_minority_2011, data = bei_df))
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*ct$"), matches("^pct_esp_vert_diff.*ct$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b250$"), matches("^pct_esp_vert_diff.*b250$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b500$"), matches("^pct_esp_vert_diff.*b500$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b750$"), matches("^pct_esp_vert_diff.*b750$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Looking at objective #1 | do urban interventions tend to be located in low SES neighborhoods?. We look at \[Urban Condition_{2011} = f(SES_{2011})\] as well as \[Urban Condition_{2011} = f(Gentrification_{2011 \to 2016})\]
Here \(UrbanCondition\) means the state of the urban environment features, such as length of bike lanes, greenness coverage, etc. This needs to be distinguished from \(UrbanIntervention\), which accounts for the changes in the \(UrbanConditions\) (see below).
# keep only interact CT
bei_df_aoi <- filter(bei_df, interact_aoi)
Bike lane ratio to streets (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.200 -7.709 -2.064 5.143 59.255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8868 0.3611 24.613 < 2e-16 ***
## wSCOREMAT.2011 -27.1407 8.8021 -3.083 0.00213 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.409 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01363, Adjusted R-squared: 0.0122
## F-statistic: 9.508 on 1 and 688 DF, p-value: 0.002128
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.4394 -5.3193 -0.8782 3.8947 24.9430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9081 0.2544 35.019 < 2e-16 ***
## wSCOREMAT.2011 -33.4497 6.2014 -5.394 9.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.629 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04057, Adjusted R-squared: 0.03918
## F-statistic: 29.09 on 1 and 688 DF, p-value: 9.492e-08
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7672 -3.9200 -0.7691 3.1333 20.7230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9691 0.2162 41.488 < 2e-16 ***
## wSCOREMAT.2011 -31.0529 5.2703 -5.892 5.97e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.634 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04804, Adjusted R-squared: 0.04665
## F-statistic: 34.72 on 1 and 688 DF, p-value: 5.971e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5371 -3.5685 -0.5231 2.4075 18.3871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9986 0.1938 46.431 < 2e-16 ***
## wSCOREMAT.2011 -26.8673 4.7248 -5.686 1.92e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.05 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04489, Adjusted R-squared: 0.0435
## F-statistic: 32.34 on 1 and 688 DF, p-value: 1.918e-08
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.859 -11.624 -0.331 8.977 47.657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.9617 0.5933 63.988 < 2e-16 ***
## wSCOREMAT.2011 -109.4130 14.4631 -7.565 1.25e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.46 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07679, Adjusted R-squared: 0.07545
## F-statistic: 57.23 on 1 and 688 DF, p-value: 1.247e-13
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.973 -8.235 -0.954 7.170 41.284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.0871 0.4897 73.690 < 2e-16 ***
## wSCOREMAT.2011 -90.8096 11.9387 -7.606 9.29e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.76 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07757, Adjusted R-squared: 0.07623
## F-statistic: 57.86 on 1 and 688 DF, p-value: 9.289e-14
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.955 -8.076 -0.976 7.123 40.579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.1464 0.4645 77.820 < 2e-16 ***
## wSCOREMAT.2011 -83.2220 11.3236 -7.349 5.66e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.1 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07279, Adjusted R-squared: 0.07145
## F-statistic: 54.01 on 1 and 688 DF, p-value: 5.662e-13
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.621 -8.282 -0.894 7.305 39.867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.211 0.451 80.287 < 2e-16 ***
## wSCOREMAT.2011 -76.126 10.995 -6.923 1.01e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.75 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06513, Adjusted R-squared: 0.06377
## F-statistic: 47.93 on 1 and 688 DF, p-value: 1.013e-11
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.954 -5.492 -0.802 4.630 44.704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.8069 0.3379 55.66 <2e-16 ***
## wSCOREMAT.2011 -96.7463 8.2370 -11.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.805 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.167, Adjusted R-squared: 0.1658
## F-statistic: 138 on 1 and 688 DF, p-value: < 2.2e-16
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.9166 -4.2910 -0.8451 3.4739 27.8788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.6539 0.2668 66.17 <2e-16 ***
## wSCOREMAT.2011 -81.7658 6.5045 -12.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.953 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1868, Adjusted R-squared: 0.1856
## F-statistic: 158 on 1 and 688 DF, p-value: < 2.2e-16
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.4603 -4.0485 -0.9957 3.5770 26.5204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.738 0.250 70.96 <2e-16 ***
## wSCOREMAT.2011 -76.880 6.094 -12.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.515 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1879, Adjusted R-squared: 0.1867
## F-statistic: 159.1 on 1 and 688 DF, p-value: < 2.2e-16
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.6658 -3.9100 -0.6752 3.4947 25.5210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.7572 0.2377 74.69 <2e-16 ***
## wSCOREMAT.2011 -72.9660 5.7960 -12.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.196 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1872, Adjusted R-squared: 0.186
## F-statistic: 158.5 on 1 and 688 DF, p-value: < 2.2e-16
Bike lane ratio to streets (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011ct, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.713 -7.452 -2.008 4.863 59.699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.07967 0.66409 16.684 < 2e-16 ***
## vis_minority_2011 -0.09265 0.02192 -4.226 2.7e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.289 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0253, Adjusted R-squared: 0.02388
## F-statistic: 17.86 on 1 and 688 DF, p-value: 2.702e-05
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b250, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1067 -4.7893 -0.6668 3.8226 22.6792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.54371 0.46311 24.926 < 2e-16 ***
## vis_minority_2011 -0.11042 0.01529 -7.222 1.36e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.478 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07046, Adjusted R-squared: 0.06911
## F-statistic: 52.15 on 1 and 688 DF, p-value: 1.365e-12
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b500, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6525 -3.7275 -0.6147 3.0419 20.5437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.50117 0.39113 29.405 < 2e-16 ***
## vis_minority_2011 -0.10577 0.01291 -8.191 1.26e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.471 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.08885, Adjusted R-squared: 0.08752
## F-statistic: 67.09 on 1 and 688 DF, p-value: 1.264e-15
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b750, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6227 -3.3596 -0.4224 2.5213 17.7443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.43262 0.34878 32.779 <2e-16 ***
## vis_minority_2011 -0.10094 0.01152 -8.766 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.879 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1005, Adjusted R-squared: 0.09915
## F-statistic: 76.83 on 1 and 688 DF, p-value: < 2.2e-16
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011ct, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.420 -12.261 -0.970 9.559 50.400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.98501 1.12478 38.216 < 2e-16 ***
## vis_minority_2011 -0.22010 0.03713 -5.927 4.87e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.73 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04858, Adjusted R-squared: 0.0472
## F-statistic: 35.13 on 1 and 688 DF, p-value: 4.873e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b250, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.184 -9.310 -1.106 7.525 44.699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.79001 0.93414 42.595 < 2e-16 ***
## vis_minority_2011 -0.16391 0.03084 -5.315 1.45e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.07 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03944, Adjusted R-squared: 0.03804
## F-statistic: 28.25 on 1 and 688 DF, p-value: 1.445e-07
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b500, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.877 -9.260 -1.111 7.543 41.841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.2416 0.8876 44.212 < 2e-16 ***
## vis_minority_2011 -0.1386 0.0293 -4.732 2.71e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.42 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03151, Adjusted R-squared: 0.03011
## F-statistic: 22.39 on 1 and 688 DF, p-value: 2.706e-06
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b750, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.932 -9.202 -0.917 7.572 40.308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.87252 0.86145 45.124 < 2e-16 ***
## vis_minority_2011 -0.12045 0.02844 -4.235 2.6e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.05 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02541, Adjusted R-squared: 0.02399
## F-statistic: 17.93 on 1 and 688 DF, p-value: 2.597e-05
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011ct, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.484 -6.304 -1.788 4.737 49.585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.84613 0.67054 32.580 < 2e-16 ***
## vis_minority_2011 -0.13945 0.02214 -6.299 5.34e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.379 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05453, Adjusted R-squared: 0.05315
## F-statistic: 39.68 on 1 and 688 DF, p-value: 5.342e-10
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b250, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.891 -5.227 -1.552 3.870 30.072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.68093 0.54102 36.377 <2e-16 ***
## vis_minority_2011 -0.09617 0.01786 -5.384 1e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.568 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04043, Adjusted R-squared: 0.03903
## F-statistic: 28.99 on 1 and 688 DF, p-value: 1.001e-07
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b500, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.866 -4.947 -1.760 3.788 30.662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.21085 0.51074 37.614 < 2e-16 ***
## vis_minority_2011 -0.07341 0.01686 -4.353 1.54e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.144 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02681, Adjusted R-squared: 0.02539
## F-statistic: 18.95 on 1 and 688 DF, p-value: 1.544e-05
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b750, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.188 -4.538 -1.596 3.704 28.055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.90064 0.48742 38.777 < 2e-16 ***
## vis_minority_2011 -0.05982 0.01609 -3.718 0.000218 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.818 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01969, Adjusted R-squared: 0.01827
## F-statistic: 13.82 on 1 and 688 DF, p-value: 0.0002175
Gentrified CT between 2011 and 2016
Bike lane ratio to streets (in %)
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane.by.street.2011ct, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011ct, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011ct, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 5 4.95 0.056 0.813
## Residuals 688 60902 88.52
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane.by.street.2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.760 -8.575 -1.815 5.186 55.344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.7600 0.4285 20.441 <2e-16 ***
## gentrified_2016_2011TRUE -0.1846 0.7805 -0.236 0.813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.409 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 8.126e-05, Adjusted R-squared: -0.001372
## F-statistic: 0.05591 on 1 and 688 DF, p-value: 0.8131
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane.by.street.2011b250, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b250, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b250, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 2 1.67 0.037 0.847
## Residuals 688 31057 45.14
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane.by.street.2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.7878 -4.9870 -0.6935 4.1027 23.9655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6805 0.3060 28.365 <2e-16 ***
## gentrified_2016_2011TRUE 0.1073 0.5574 0.192 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.719 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 5.382e-05, Adjusted R-squared: -0.0014
## F-statistic: 0.03703 on 1 and 688 DF, p-value: 0.8475
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane.by.street.2011b500, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b500, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b500, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 22 21.78 0.664 0.416
## Residuals 688 22580 32.82
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane.by.street.2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.060 -4.157 -0.576 3.120 19.585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6728 0.2609 33.236 <2e-16 ***
## gentrified_2016_2011TRUE 0.3871 0.4753 0.815 0.416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.729 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0009635, Adjusted R-squared: -0.0004886
## F-statistic: 0.6635 on 1 and 688 DF, p-value: 0.4156
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane.by.street.2011b750, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b750, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b750, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 41 40.84 1.547 0.214
## Residuals 688 18163 26.40
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane.by.street.2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.2152 -3.8001 -0.7224 2.6198 19.1075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6850 0.2340 37.110 <2e-16 ***
## gentrified_2016_2011TRUE 0.5302 0.4263 1.244 0.214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.138 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.002243, Adjusted R-squared: 0.0007931
## F-statistic: 1.547 on 1 and 688 DF, p-value: 0.214
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_2011ct, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011ct, na.rm = TRUE),
sd = sd(pct_esp_vert_2011ct, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 23284 23284 102.9 <2e-16 ***
## Residuals 688 155714 226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.998 -10.293 -0.814 8.654 50.792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.1580 0.6852 60.06 <2e-16 ***
## gentrified_2016_2011TRUE -12.6588 1.2481 -10.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.04 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1301, Adjusted R-squared: 0.1288
## F-statistic: 102.9 on 1 and 688 DF, p-value: < 2.2e-16
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_2011b250, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b250, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b250, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 14776 14776 94.56 <2e-16 ***
## Residuals 688 107511 156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.278 -8.132 -1.213 7.587 40.172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.6277 0.5694 67.841 <2e-16 ***
## gentrified_2016_2011TRUE -10.0844 1.0371 -9.724 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.5 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1208, Adjusted R-squared: 0.1196
## F-statistic: 94.56 on 1 and 688 DF, p-value: < 2.2e-16
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_2011b500, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b500, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b500, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 12682 12682 90.12 <2e-16 ***
## Residuals 688 96815 141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.213 -7.841 -1.241 7.464 38.087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.5032 0.5403 71.260 <2e-16 ***
## gentrified_2016_2011TRUE -9.3426 0.9841 -9.493 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.86 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1158, Adjusted R-squared: 0.1145
## F-statistic: 90.12 on 1 and 688 DF, p-value: < 2.2e-16
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_2011b750, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b750, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b750, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 11029 11029 82.95 <2e-16 ***
## Residuals 688 91471 133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.141 -7.901 -1.059 7.329 37.659
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.4109 0.5252 73.136 <2e-16 ***
## gentrified_2016_2011TRUE -8.7122 0.9566 -9.108 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.53 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1076, Adjusted R-squared: 0.1063
## F-statistic: 82.95 on 1 and 688 DF, p-value: < 2.2e-16
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_high_2011ct, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011ct, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011ct, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1856 1855.8 20.54 6.89e-06 ***
## Residuals 688 62160 90.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_high_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.448 -6.230 -1.735 4.263 49.252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.3483 0.4329 44.690 < 2e-16 ***
## gentrified_2016_2011TRUE -3.5738 0.7886 -4.532 6.89e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.505 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02899, Adjusted R-squared: 0.02758
## F-statistic: 20.54 on 1 and 688 DF, p-value: 6.886e-06
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_high_2011b250, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b250, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b250, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 786 786.4 13.43 0.000266 ***
## Residuals 688 40275 58.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_high_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.707 -5.225 -1.519 3.693 31.513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.9168 0.3485 51.411 < 2e-16 ***
## gentrified_2016_2011TRUE -2.3264 0.6347 -3.665 0.000266 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.651 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01915, Adjusted R-squared: 0.01773
## F-statistic: 13.43 on 1 and 688 DF, p-value: 0.0002662
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_high_2011b500, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b500, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b500, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 632 632.0 12.27 0.000491 ***
## Residuals 688 35449 51.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_high_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.868 -4.719 -1.365 3.895 31.012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.9576 0.3270 54.924 < 2e-16 ***
## gentrified_2016_2011TRUE -2.0855 0.5955 -3.502 0.000491 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.178 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01752, Adjusted R-squared: 0.01609
## F-statistic: 12.27 on 1 and 688 DF, p-value: 0.0004913
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_high_2011b750, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b750, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b750, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 476 476.4 10.2 0.00147 **
## Residuals 688 32147 46.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_high_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.393 -4.715 -1.352 3.662 28.307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.9127 0.3114 57.532 < 2e-16 ***
## gentrified_2016_2011TRUE -1.8107 0.5671 -3.193 0.00147 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.836 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0146, Adjusted R-squared: 0.01317
## F-statistic: 10.19 on 1 and 688 DF, p-value: 0.001473
Looking at objective #1 | do urban interventions tend to be located in low SES neighborhoods?. We look at \[Urban Intervention_{2011 \to 2016} = f(SES_{2011})\] as well as \[Urban Intervention_{2011 \to 2016} = f(Gentrification_{2011 \to 2016})\]
Here \(Urban Intervention\) means the changes in the urban environment features, such as variation of bike lane length, greenness coverage, etc.
# keep only interact CT
bei_df_aoi <- filter(bei_df, interact_aoi)
Bike lane ratio to streets (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016ct ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.442 -3.695 -3.621 0.992 38.452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6795 0.2761 13.328 <2e-16 ***
## wSCOREMAT.2011 -1.2310 6.7303 -0.183 0.855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.194 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 4.862e-05, Adjusted R-squared: -0.001405
## F-statistic: 0.03345 on 1 and 688 DF, p-value: 0.8549
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016ct ~ wSCOREMAT.2011 + Bike_lane.by.street.2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016ct ~ wSCOREMAT.2011 +
## Bike_lane.by.street.2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.601 -4.253 -3.044 1.520 37.493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.62603 0.37517 12.331 < 2e-16 ***
## wSCOREMAT.2011 -4.12185 6.71541 -0.614 0.539558
## Bike_lane.by.street.2011ct -0.10651 0.02889 -3.687 0.000245 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.129 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01945, Adjusted R-squared: 0.0166
## F-statistic: 6.815 on 2 and 687 DF, p-value: 0.001173
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b250 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.425 -3.455 -2.437 1.626 27.938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4854 0.2095 16.637 <2e-16 ***
## wSCOREMAT.2011 -5.3511 5.1073 -1.048 0.295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.459 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.001593, Adjusted R-squared: 0.0001418
## F-statistic: 1.098 on 1 and 688 DF, p-value: 0.2951
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b250 ~ wSCOREMAT.2011 + Bike_lane.by.street.2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b250 ~ wSCOREMAT.2011 +
## Bike_lane.by.street.2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.103 -3.597 -1.898 1.815 26.261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.20649 0.33995 15.315 < 2e-16 ***
## wSCOREMAT.2011 -11.81363 5.07233 -2.329 0.0201 *
## Bike_lane.by.street.2011b250 -0.19320 0.03054 -6.325 4.55e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.311 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05654, Adjusted R-squared: 0.05379
## F-statistic: 20.59 on 2 and 687 DF, p-value: 2.078e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b500 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.367 -3.310 -1.827 2.005 18.540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4779 0.1803 19.29 <2e-16 ***
## wSCOREMAT.2011 -6.4606 4.3960 -1.47 0.142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.699 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.00313, Adjusted R-squared: 0.001681
## F-statistic: 2.16 on 1 and 688 DF, p-value: 0.1421
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b500 ~ wSCOREMAT.2011 + Bike_lane.by.street.2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b500 ~ wSCOREMAT.2011 +
## Bike_lane.by.street.2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.405 -3.254 -1.529 2.070 18.630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.15019 0.32914 15.648 < 2e-16 ***
## wSCOREMAT.2011 -12.25032 4.39472 -2.788 0.00546 **
## Bike_lane.by.street.2011b500 -0.18645 0.03102 -6.011 2.99e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.583 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05294, Adjusted R-squared: 0.05018
## F-statistic: 19.2 on 2 and 687 DF, p-value: 7.689e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b750 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.565 -3.039 -1.415 1.939 18.956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4705 0.1622 21.40 <2e-16 ***
## wSCOREMAT.2011 -5.5739 3.9538 -1.41 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.226 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.00288, Adjusted R-squared: 0.001431
## F-statistic: 1.987 on 1 and 688 DF, p-value: 0.1591
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b750 ~ wSCOREMAT.2011 + Bike_lane.by.street.2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b750 ~ wSCOREMAT.2011 +
## Bike_lane.by.street.2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.941 -3.024 -1.105 1.910 18.018
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.12804 0.32188 15.931 < 2e-16 ***
## wSCOREMAT.2011 -10.52276 3.94933 -2.664 0.00789 **
## Bike_lane.by.street.2011b750 -0.18420 0.03114 -5.914 5.25e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.126 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05119, Adjusted R-squared: 0.04843
## F-statistic: 18.53 on 2 and 687 DF, p-value: 1.449e-08
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017ct ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.165 -1.944 -0.273 1.524 37.029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1619 0.1313 31.687 <2e-16 ***
## wSCOREMAT.2011 1.7219 3.2020 0.538 0.591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.423 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0004201, Adjusted R-squared: -0.001033
## F-statistic: 0.2892 on 1 and 688 DF, p-value: 0.5909
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017ct ~ wSCOREMAT.2011 + pct_esp_vert_2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017ct ~ wSCOREMAT.2011 +
## pct_esp_vert_2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.137 -1.684 -0.296 1.369 36.968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.936893 0.338723 17.527 < 2e-16 ***
## wSCOREMAT.2011 -3.393906 3.259702 -1.041 0.298
## pct_esp_vert_2011ct -0.046757 0.008256 -5.663 2.18e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.348 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04501, Adjusted R-squared: 0.04223
## F-statistic: 16.19 on 2 and 687 DF, p-value: 1.35e-07
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b250 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.827 -1.729 -0.027 1.341 34.699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9602 0.1125 35.192 <2e-16 ***
## wSCOREMAT.2011 -1.7145 2.7434 -0.625 0.532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.932 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0005674, Adjusted R-squared: -0.0008853
## F-statistic: 0.3906 on 1 and 688 DF, p-value: 0.5322
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b250 ~ wSCOREMAT.2011 + pct_esp_vert_2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b250 ~ wSCOREMAT.2011 +
## pct_esp_vert_2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.958 -1.514 -0.185 1.094 34.802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.95375 0.32597 18.265 < 2e-16 ***
## wSCOREMAT.2011 -6.73103 2.77465 -2.426 0.0155 *
## pct_esp_vert_2011b250 -0.05524 0.00851 -6.492 1.63e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.849 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05833, Adjusted R-squared: 0.05559
## F-statistic: 21.28 on 2 and 687 DF, p-value: 1.082e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b500 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.614 -1.542 -0.055 1.293 32.079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8940 0.1049 37.116 <2e-16 ***
## wSCOREMAT.2011 -1.2350 2.5576 -0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.734 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0003388, Adjusted R-squared: -0.001114
## F-statistic: 0.2332 on 1 and 688 DF, p-value: 0.6293
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b500 ~ wSCOREMAT.2011 + pct_esp_vert_2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b500 ~ wSCOREMAT.2011 +
## pct_esp_vert_2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.817 -1.482 -0.186 1.122 32.262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.75759 0.32003 17.991 < 2e-16 ***
## wSCOREMAT.2011 -5.52570 2.58791 -2.135 0.0331 *
## pct_esp_vert_2011b500 -0.05156 0.00839 -6.145 1.35e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.664 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05242, Adjusted R-squared: 0.04967
## F-statistic: 19 on 2 and 687 DF, p-value: 9.264e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b750 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6691 -1.5113 -0.0059 1.2007 29.1958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8425 0.1008 38.102 <2e-16 ***
## wSCOREMAT.2011 -1.7355 2.4585 -0.706 0.48
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.628 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0007238, Adjusted R-squared: -0.0007286
## F-statistic: 0.4983 on 1 and 688 DF, p-value: 0.4805
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b750 ~ wSCOREMAT.2011 + pct_esp_vert_2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b750 ~ wSCOREMAT.2011 +
## pct_esp_vert_2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4480 -1.4348 -0.1886 1.1088 29.4180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.619194 0.317052 17.723 < 2e-16 ***
## wSCOREMAT.2011 -5.470665 2.482528 -2.204 0.0279 *
## pct_esp_vert_2011b750 -0.049066 0.008323 -5.895 5.86e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.566 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04884, Adjusted R-squared: 0.04607
## F-statistic: 17.64 on 2 and 687 DF, p-value: 3.385e-08
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017ct ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8508 -1.4234 -0.3206 1.1810 11.3606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4314 0.0846 40.560 < 2e-16 ***
## wSCOREMAT.2011 -11.9113 2.0625 -5.775 1.16e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.205 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04624, Adjusted R-squared: 0.04485
## F-statistic: 33.35 on 1 and 688 DF, p-value: 1.164e-08
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017ct ~ wSCOREMAT.2011 + pct_esp_vert_high_2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017ct ~ wSCOREMAT.2011 +
## pct_esp_vert_high_2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.857 -1.451 -0.343 1.155 10.810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.945265 0.197553 14.909 < 2e-16 ***
## wSCOREMAT.2011 -9.410419 2.249397 -4.184 3.24e-05 ***
## pct_esp_vert_high_2011ct 0.025850 0.009502 2.720 0.00668 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.194 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0564, Adjusted R-squared: 0.05365
## F-statistic: 20.53 on 2 and 687 DF, p-value: 2.184e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b250 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9628 -1.1429 -0.2624 0.8870 6.8083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.15716 0.06809 46.369 < 2e-16 ***
## wSCOREMAT.2011 -12.18518 1.65991 -7.341 6.01e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.774 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07264, Adjusted R-squared: 0.07129
## F-statistic: 53.89 on 1 and 688 DF, p-value: 6.007e-13
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b250 ~ wSCOREMAT.2011 + pct_esp_vert_high_2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b250 ~ wSCOREMAT.2011 +
## pct_esp_vert_high_2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4856 -1.1112 -0.2855 0.8853 6.7932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.694806 0.183921 14.652 < 2e-16 ***
## wSCOREMAT.2011 -10.043738 1.832306 -5.481 5.93e-08 ***
## pct_esp_vert_high_2011b250 0.026190 0.009685 2.704 0.00702 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.766 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0824, Adjusted R-squared: 0.07973
## F-statistic: 30.85 on 2 and 687 DF, p-value: 1.482e-13
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b500 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0396 -1.0949 -0.2033 0.7381 7.3251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.07086 0.06356 48.316 < 2e-16 ***
## wSCOREMAT.2011 -10.94933 1.54945 -7.067 3.9e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.656 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06767, Adjusted R-squared: 0.06632
## F-statistic: 49.94 on 1 and 688 DF, p-value: 3.902e-12
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b500 ~ wSCOREMAT.2011 + pct_esp_vert_high_2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b500 ~ wSCOREMAT.2011 +
## pct_esp_vert_high_2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8059 -1.0936 -0.1912 0.6904 7.2757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.558316 0.182249 14.037 < 2e-16 ***
## wSCOREMAT.2011 -8.727890 1.709430 -5.106 4.27e-07 ***
## pct_esp_vert_high_2011b500 0.028895 0.009637 2.998 0.00281 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.647 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07971, Adjusted R-squared: 0.07703
## F-statistic: 29.75 on 2 and 687 DF, p-value: 4.051e-13
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b750 ~ wSCOREMAT.2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9569 -0.9961 -0.2127 0.7393 6.2521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.99802 0.05853 51.218 < 2e-16 ***
## wSCOREMAT.2011 -10.33948 1.42699 -7.246 1.16e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.525 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0709, Adjusted R-squared: 0.06955
## F-statistic: 52.5 on 1 and 688 DF, p-value: 1.158e-12
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b750 ~ wSCOREMAT.2011 + pct_esp_vert_high_2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b750 ~ wSCOREMAT.2011 +
## pct_esp_vert_high_2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7591 -0.9842 -0.2096 0.6951 6.2118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.439850 0.175338 13.915 < 2e-16 ***
## wSCOREMAT.2011 -8.045919 1.571026 -5.121 3.94e-07 ***
## pct_esp_vert_high_2011b750 0.031433 0.009316 3.374 0.000783 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.514 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.08604, Adjusted R-squared: 0.08338
## F-statistic: 32.34 on 2 and 687 DF, p-value: 3.786e-14
Bike lane ratio to streets (in %). With or without controlling for UC in 2011.
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016ct, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016ct ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.758 -3.975 -3.059 0.859 38.865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.85228 0.51159 9.485 < 2e-16 ***
## vis_minority_2011 -0.04599 0.01689 -2.723 0.00663 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.156 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01066, Adjusted R-squared: 0.009226
## F-statistic: 7.416 on 1 and 688 DF, p-value: 0.00663
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016ct ~ vis_minority_2011 + Bike_lane.by.street.2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016ct ~ vis_minority_2011 +
## Bike_lane.by.street.2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.788 -4.145 -2.856 1.391 37.922
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.17860 0.59938 10.308 < 2e-16 ***
## vis_minority_2011 -0.05708 0.01691 -3.375 0.000779 ***
## Bike_lane.by.street.2011ct -0.11971 0.02903 -4.123 4.2e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.074 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03455, Adjusted R-squared: 0.03174
## F-statistic: 12.29 on 2 and 687 DF, p-value: 5.679e-06
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016b250, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b250 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.625 -3.447 -2.147 1.972 27.232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.86710 0.38548 12.626 < 2e-16 ***
## vis_minority_2011 -0.05521 0.01273 -4.338 1.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.392 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02662, Adjusted R-squared: 0.02521
## F-statistic: 18.82 on 1 and 688 DF, p-value: 1.655e-05
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b250 ~ vis_minority_2011 + Bike_lane.by.street.2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b250 ~ vis_minority_2011 +
## Bike_lane.by.street.2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.902 -3.524 -1.576 2.039 24.834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.53777 0.51119 14.745 < 2e-16 ***
## vis_minority_2011 -0.08075 0.01269 -6.364 3.60e-10 ***
## Bike_lane.by.street.2011b250 -0.23135 0.03051 -7.584 1.09e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.183 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1018, Adjusted R-squared: 0.0992
## F-statistic: 38.94 on 2 and 687 DF, p-value: < 2.2e-16
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016b500, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b500 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.704 -3.241 -1.655 2.003 18.311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.59890 0.33235 13.837 < 2e-16 ***
## vis_minority_2011 -0.04575 0.01097 -4.169 3.45e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.649 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02464, Adjusted R-squared: 0.02323
## F-statistic: 17.38 on 1 and 688 DF, p-value: 3.446e-05
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b500 ~ vis_minority_2011 + Bike_lane.by.street.2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b500 ~ vis_minority_2011 +
## Bike_lane.by.street.2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.221 -3.234 -1.230 1.929 18.372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.19708 0.48166 14.942 < 2e-16 ***
## vis_minority_2011 -0.06964 0.01109 -6.280 6.01e-10 ***
## Bike_lane.by.street.2011b500 -0.22591 0.03125 -7.229 1.30e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.485 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.09358, Adjusted R-squared: 0.09094
## F-statistic: 35.46 on 2 and 687 DF, p-value: 2.199e-15
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane_diff.by.street.2011.2016b750, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b750 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.988 -3.000 -1.417 1.921 18.327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.45024 0.29897 14.885 < 2e-16 ***
## vis_minority_2011 -0.04016 0.00987 -4.068 5.29e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.182 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02349, Adjusted R-squared: 0.02207
## F-statistic: 16.55 on 1 and 688 DF, p-value: 5.286e-05
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b750 ~ vis_minority_2011 + Bike_lane.by.street.2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b750 ~ vis_minority_2011 +
## Bike_lane.by.street.2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.973 -2.915 -1.020 1.842 16.865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.07360 0.46138 15.331 < 2e-16 ***
## vis_minority_2011 -0.06332 0.01003 -6.310 5.01e-10 ***
## Bike_lane.by.street.2011b750 -0.22946 0.03151 -7.282 9.02e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.032 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.09347, Adjusted R-squared: 0.09083
## F-statistic: 35.42 on 2 and 687 DF, p-value: 2.296e-15
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017ct, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017ct ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.093 -1.938 -0.199 1.553 36.892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.257351 0.244837 17.39 <2e-16 ***
## vis_minority_2011 -0.004040 0.008083 -0.50 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.425 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.000363, Adjusted R-squared: -0.00109
## F-statistic: 0.2498 on 1 and 688 DF, p-value: 0.6174
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017ct ~ vis_minority_2011 + pct_esp_vert_2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017ct ~ vis_minority_2011 +
## pct_esp_vert_2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.326 -1.777 -0.310 1.361 36.775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.230912 0.423236 14.722 < 2e-16 ***
## vis_minority_2011 -0.014146 0.008107 -1.745 0.0814 .
## pct_esp_vert_2011ct -0.045913 0.008118 -5.656 2.28e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.35 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04484, Adjusted R-squared: 0.04205
## F-statistic: 16.12 on 2 and 687 DF, p-value: 1.435e-07
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017b250, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b250 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.745 -1.678 -0.043 1.310 34.485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.253273 0.209119 20.339 <2e-16 ***
## vis_minority_2011 -0.011863 0.006904 -1.718 0.0862 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.925 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.004273, Adjusted R-squared: 0.002825
## F-statistic: 2.952 on 1 and 688 DF, p-value: 0.08621
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b250 ~ vis_minority_2011 + pct_esp_vert_2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b250 ~ vis_minority_2011 +
## pct_esp_vert_2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.593 -1.584 -0.145 1.141 34.553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.400991 0.387333 16.526 < 2e-16 ***
## vis_minority_2011 -0.020710 0.006842 -3.027 0.00256 **
## pct_esp_vert_2011b250 -0.053976 0.008289 -6.512 1.43e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.841 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06216, Adjusted R-squared: 0.05943
## F-statistic: 22.77 on 2 and 687 DF, p-value: 2.668e-10
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017b500, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b500 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.352 -1.594 -0.054 1.310 31.816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.225329 0.194825 21.688 <2e-16 ***
## vis_minority_2011 -0.013070 0.006432 -2.032 0.0425 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.725 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.005966, Adjusted R-squared: 0.004521
## F-statistic: 4.129 on 1 and 688 DF, p-value: 0.04253
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b500 ~ vis_minority_2011 + pct_esp_vert_2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b500 ~ vis_minority_2011 +
## pct_esp_vert_2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.510 -1.493 -0.199 1.206 31.983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.241340 0.371498 16.800 < 2e-16 ***
## vis_minority_2011 -0.020193 0.006359 -3.176 0.00156 **
## pct_esp_vert_2011b500 -0.051374 0.008142 -6.310 5e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.651 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06042, Adjusted R-squared: 0.05768
## F-statistic: 22.09 on 2 and 687 DF, p-value: 5.046e-10
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_2011.2017b750, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_2011.2017b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b750 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6430 -1.5699 -0.0092 1.2938 28.8848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.242668 0.186996 22.69 <2e-16 ***
## vis_minority_2011 -0.015868 0.006174 -2.57 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.616 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.009511, Adjusted R-squared: 0.008071
## F-statistic: 6.606 on 1 and 688 DF, p-value: 0.01037
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b750 ~ vis_minority_2011 + pct_esp_vert_2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b750 ~ vis_minority_2011 +
## pct_esp_vert_2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6188 -1.4098 -0.1884 1.1195 29.1015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.148210 0.362749 16.949 < 2e-16 ***
## vis_minority_2011 -0.021772 0.006096 -3.571 0.00038 ***
## pct_esp_vert_2011b750 -0.049020 0.008068 -6.076 2.04e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.55 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06002, Adjusted R-squared: 0.05729
## F-statistic: 21.93 on 2 and 687 DF, p-value: 5.83e-10
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017ct, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017ct ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017ct ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7482 -1.5184 -0.2624 1.1531 10.7915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.990009 0.159253 25.055 < 2e-16 ***
## vis_minority_2011 -0.024453 0.005258 -4.651 3.96e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.228 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03048, Adjusted R-squared: 0.02907
## F-statistic: 21.63 on 1 and 688 DF, p-value: 3.964e-06
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017ct ~ vis_minority_2011 + pct_esp_vert_high_2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017ct ~ vis_minority_2011 +
## pct_esp_vert_high_2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7699 -1.4988 -0.3498 1.1095 10.2885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.200485 0.251171 12.742 < 2e-16 ***
## vis_minority_2011 -0.019413 0.005348 -3.630 0.000305 ***
## pct_esp_vert_high_2011ct 0.036140 0.008956 4.035 6.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.203 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05293, Adjusted R-squared: 0.05017
## F-statistic: 19.2 on 2 and 687 DF, p-value: 7.708e-09
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017b250, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b250 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b250 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0818 -1.2094 -0.2710 0.9444 6.9013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.657494 0.129117 28.327 < 2e-16 ***
## vis_minority_2011 -0.021890 0.004263 -5.135 3.67e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.806 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03691, Adjusted R-squared: 0.03551
## F-statistic: 26.37 on 1 and 688 DF, p-value: 3.673e-07
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b250 ~ vis_minority_2011 + pct_esp_vert_high_2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b250 ~ vis_minority_2011 +
## pct_esp_vert_high_2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1679 -1.1641 -0.2817 0.8024 6.8128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.843690 0.217583 13.069 < 2e-16 ***
## vis_minority_2011 -0.017914 0.004289 -4.177 3.34e-05 ***
## pct_esp_vert_high_2011b250 0.041350 0.008967 4.611 4.78e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.78 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06583, Adjusted R-squared: 0.06311
## F-statistic: 24.2 on 2 and 687 DF, p-value: 6.948e-11
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017b500, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b500 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b500 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2936 -1.1328 -0.2707 0.8096 7.3055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.474949 0.120735 28.782 < 2e-16 ***
## vis_minority_2011 -0.017844 0.003986 -4.477 8.87e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.689 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0283, Adjusted R-squared: 0.02689
## F-statistic: 20.04 on 1 and 688 DF, p-value: 8.873e-06
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b500 ~ vis_minority_2011 + pct_esp_vert_high_2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b500 ~ vis_minority_2011 +
## pct_esp_vert_high_2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4292 -1.0788 -0.2186 0.7476 7.1924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.624779 0.207495 12.650 < 2e-16 ***
## vis_minority_2011 -0.014596 0.003972 -3.675 0.000257 ***
## pct_esp_vert_high_2011b500 0.044255 0.008859 4.995 7.46e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.66 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06236, Adjusted R-squared: 0.05963
## F-statistic: 22.84 on 2 and 687 DF, p-value: 2.481e-10
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_diff_high_2011.2017b750, x=vis_minority_2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b750 ~ vis_minority_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b750 ~ vis_minority_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1753 -1.0349 -0.2860 0.7644 6.2564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.347398 0.111550 30.008 < 2e-16 ***
## vis_minority_2011 -0.015569 0.003683 -4.228 2.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.56 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02532, Adjusted R-squared: 0.0239
## F-statistic: 17.87 on 1 and 688 DF, p-value: 2.681e-05
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b750 ~ vis_minority_2011 + pct_esp_vert_high_2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b750 ~ vis_minority_2011 +
## pct_esp_vert_high_2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3455 -1.0266 -0.2463 0.6975 6.1498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.458366 0.194986 12.608 < 2e-16 ***
## vis_minority_2011 -0.012756 0.003643 -3.502 0.000493 ***
## pct_esp_vert_high_2011b750 0.047037 0.008545 5.505 5.23e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.528 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06649, Adjusted R-squared: 0.06378
## F-statistic: 24.47 on 2 and 687 DF, p-value: 5.437e-11
Gentrified CT between 2011 and 2016
Bike lane ratio to streets (in %)
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane_diff.by.street.2011.2016ct, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016ct, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 843 843.4 16.69 4.92e-05 ***
## Residuals 688 34767 50.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.724 -2.947 -2.947 0.662 39.191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9468 0.3238 9.101 < 2e-16 ***
## gentrified_2016_2011TRUE 2.4092 0.5897 4.085 4.92e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.109 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02368, Adjusted R-squared: 0.02226
## F-statistic: 16.69 on 1 and 688 DF, p-value: 4.921e-05
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011 + Bike_lane.by.street.2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011 +
## Bike_lane.by.street.2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.898 -3.850 -2.671 1.060 38.291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.84970 0.40696 9.460 < 2e-16 ***
## gentrified_2016_2011TRUE 2.39022 0.58467 4.088 4.86e-05 ***
## Bike_lane.by.street.2011ct -0.10307 0.02856 -3.609 0.000329 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.047 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04185, Adjusted R-squared: 0.03906
## F-statistic: 15 on 2 and 687 DF, p-value: 4.19e-07
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane_diff.by.street.2011.2016b250, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b250, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1124 1123.9 39.8 5.03e-10 ***
## Residuals 688 19426 28.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.617 -2.613 -2.285 1.770 26.114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6134 0.2420 10.798 < 2e-16 ***
## gentrified_2016_2011TRUE 2.7811 0.4408 6.309 5.03e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.314 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05469, Adjusted R-squared: 0.05332
## F-statistic: 39.8 on 1 and 688 DF, p-value: 5.027e-10
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011 + Bike_lane.by.street.2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011 +
## Bike_lane.by.street.2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.345 -3.320 -1.535 1.761 24.816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.18653 0.34725 12.056 < 2e-16 ***
## gentrified_2016_2011TRUE 2.80059 0.42942 6.522 1.35e-10 ***
## Bike_lane.by.street.2011b250 -0.18123 0.02937 -6.170 1.16e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.176 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1043, Adjusted R-squared: 0.1017
## F-statistic: 40.01 on 2 and 687 DF, p-value: < 2.2e-16
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane_diff.by.street.2011.2016b500, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b500, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 965 965.1 46.5 2.01e-11 ***
## Residuals 688 14280 20.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.615 -2.649 -1.577 1.713 18.848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6491 0.2075 12.766 < 2e-16 ***
## gentrified_2016_2011TRUE 2.5772 0.3779 6.819 2.01e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.556 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06331, Adjusted R-squared: 0.06195
## F-statistic: 46.5 on 1 and 688 DF, p-value: 2.008e-11
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011 + Bike_lane.by.street.2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011 +
## Bike_lane.by.street.2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.993 -2.865 -1.310 1.756 18.065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.15807 0.32708 12.713 < 2e-16 ***
## gentrified_2016_2011TRUE 2.64460 0.36924 7.162 2.05e-12 ***
## Bike_lane.by.street.2011b500 -0.17399 0.02961 -5.877 6.52e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.449 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1081, Adjusted R-squared: 0.1055
## F-statistic: 41.65 on 2 and 687 DF, p-value: < 2.2e-16
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=Bike_lane_diff.by.street.2011.2016b750, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b750, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 759 758.6 45.14 3.85e-11 ***
## Residuals 688 11563 16.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.893 -2.732 -1.275 1.702 17.528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7319 0.1867 14.630 < 2e-16 ***
## gentrified_2016_2011TRUE 2.2850 0.3401 6.719 3.85e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.1 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06157, Adjusted R-squared: 0.06021
## F-statistic: 45.14 on 1 and 688 DF, p-value: 3.848e-11
# Accounting for UC in 2011
res.lm <- lm(Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011 + Bike_lane.by.street.2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011 +
## Bike_lane.by.street.2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1464 -2.7321 -0.9453 1.7715 16.6912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.26493 0.31573 13.508 < 2e-16 ***
## gentrified_2016_2011TRUE 2.37858 0.33229 7.158 2.10e-12 ***
## Bike_lane.by.street.2011b750 -0.17651 0.02969 -5.946 4.38e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.001 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1075, Adjusted R-squared: 0.1049
## F-statistic: 41.37 on 2 and 687 DF, p-value: < 2.2e-16
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_2011.2017ct, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 97 96.85 8.355 0.00397 **
## Residuals 688 7975 11.59
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.644 -1.868 -0.258 1.425 37.232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9077 0.1551 25.20 < 2e-16 ***
## gentrified_2016_2011TRUE 0.8164 0.2825 2.89 0.00397 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.405 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.012, Adjusted R-squared: 0.01056
## F-statistic: 8.355 on 1 and 688 DF, p-value: 0.003968
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011 + pct_esp_vert_2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011 +
## pct_esp_vert_2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.455 -1.704 -0.312 1.382 37.183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.535249 0.381819 14.497 < 2e-16 ***
## gentrified_2016_2011TRUE 0.315837 0.298396 1.058 0.29
## pct_esp_vert_2011ct -0.039545 0.008502 -4.651 3.95e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.355 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04216, Adjusted R-squared: 0.03938
## F-statistic: 15.12 on 2 and 687 DF, p-value: 3.746e-07
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_2011.2017b250, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 57 56.50 6.639 0.0102 *
## Residuals 688 5855 8.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.651 -1.681 -0.051 1.254 34.949
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7612 0.1329 28.305 <2e-16 ***
## gentrified_2016_2011TRUE 0.6236 0.2420 2.577 0.0102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.917 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.009557, Adjusted R-squared: 0.008118
## F-statistic: 6.639 on 1 and 688 DF, p-value: 0.01018
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011 + pct_esp_vert_2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011 +
## pct_esp_vert_2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.849 -1.516 -0.208 1.076 35.045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.58548 0.36112 15.467 < 2e-16 ***
## gentrified_2016_2011TRUE 0.14733 0.25296 0.582 0.56
## pct_esp_vert_2011b250 -0.04723 0.00872 -5.416 8.42e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.859 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.05012, Adjusted R-squared: 0.04735
## F-statistic: 18.12 on 2 and 687 DF, p-value: 2.134e-08
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_2011.2017b500, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 59 58.84 7.967 0.0049 **
## Residuals 688 5081 7.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.458 -1.598 -0.028 1.220 32.312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6984 0.1238 29.878 <2e-16 ***
## gentrified_2016_2011TRUE 0.6364 0.2255 2.823 0.0049 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.718 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01145, Adjusted R-squared: 0.01001
## F-statistic: 7.967 on 1 and 688 DF, p-value: 0.004902
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011 + pct_esp_vert_2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011 +
## pct_esp_vert_2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.001 -1.447 -0.232 1.035 32.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.392180 0.351937 15.321 < 2e-16 ***
## gentrified_2016_2011TRUE 0.225373 0.235477 0.957 0.339
## pct_esp_vert_2011b500 -0.043991 0.008578 -5.128 3.8e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.669 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0479, Adjusted R-squared: 0.04512
## F-statistic: 17.28 on 2 and 687 DF, p-value: 4.764e-08
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_2011.2017b750, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 53 53.32 7.808 0.00535 **
## Residuals 688 4699 6.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8790 -1.5176 -0.0082 1.1328 29.4368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6532 0.1190 30.690 < 2e-16 ***
## gentrified_2016_2011TRUE 0.6058 0.2168 2.794 0.00535 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.613 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01122, Adjusted R-squared: 0.009784
## F-statistic: 7.808 on 1 and 688 DF, p-value: 0.005348
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011 + pct_esp_vert_2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011 +
## pct_esp_vert_2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.548 -1.391 -0.188 1.068 29.627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.243475 0.346926 15.114 < 2e-16 ***
## gentrified_2016_2011TRUE 0.245108 0.225808 1.085 0.278
## pct_esp_vert_2011b750 -0.041401 0.008502 -4.870 1.39e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.571 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04421, Adjusted R-squared: 0.04143
## F-statistic: 15.89 on 2 and 687 DF, p-value: 1.795e-07
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_high_2011.2017ct, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_high_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_high_2011.2017ct, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_high_2011.2017ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 106 106.33 21.42 4.4e-06 ***
## Residuals 688 3415 4.96
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_high_2011.2017ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017ct ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6852 -1.4752 -0.3852 1.1357 10.1048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1052 0.1015 30.600 < 2e-16 ***
## gentrified_2016_2011TRUE 0.8555 0.1848 4.628 4.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.228 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0302, Adjusted R-squared: 0.02879
## F-statistic: 21.42 on 1 and 688 DF, p-value: 4.404e-06
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017ct ~ gentrified_2016_2011 + pct_esp_vert_high_2011ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017ct ~ gentrified_2016_2011 +
## pct_esp_vert_high_2011ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6023 -1.4148 -0.3331 1.0547 9.9179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.095551 0.195587 10.714 < 2e-16 ***
## gentrified_2016_2011TRUE 1.041958 0.182991 5.694 1.84e-08 ***
## pct_esp_vert_high_2011ct 0.052183 0.008718 5.986 3.47e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.174 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07827, Adjusted R-squared: 0.07558
## F-statistic: 29.17 on 2 and 687 DF, p-value: 6.948e-13
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_high_2011.2017b250, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_high_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_high_2011.2017b250, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_high_2011.2017b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 78.7 78.70 24.05 1.17e-06 ***
## Residuals 688 2251.4 3.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b250 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8444 -1.2019 -0.3344 0.7941 6.9256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8744 0.0824 34.885 < 2e-16 ***
## gentrified_2016_2011TRUE 0.7360 0.1501 4.904 1.17e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.809 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03378, Adjusted R-squared: 0.03237
## F-statistic: 24.05 on 1 and 688 DF, p-value: 1.173e-06
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b250 ~ gentrified_2016_2011 + pct_esp_vert_high_2011b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b250 ~ gentrified_2016_2011 +
## pct_esp_vert_high_2011b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0087 -1.1333 -0.3036 0.7533 6.7254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.870862 0.176274 10.613 < 2e-16 ***
## gentrified_2016_2011TRUE 0.866275 0.147325 5.880 6.40e-09 ***
## pct_esp_vert_high_2011b250 0.056012 0.008764 6.391 3.03e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.759 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.088, Adjusted R-squared: 0.08535
## F-statistic: 33.15 on 2 and 687 DF, p-value: 1.811e-14
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_high_2011.2017b500, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_high_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_high_2011.2017b500, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_high_2011.2017b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 70.9 70.89 25.03 7.18e-07 ***
## Residuals 688 1948.5 2.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b500 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7769 -1.1369 -0.3169 0.7417 7.8431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.80691 0.07665 36.618 < 2e-16 ***
## gentrified_2016_2011TRUE 0.69848 0.13961 5.003 7.18e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.683 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0351, Adjusted R-squared: 0.0337
## F-statistic: 25.03 on 1 and 688 DF, p-value: 7.176e-07
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b500 ~ gentrified_2016_2011 + pct_esp_vert_high_2011b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b500 ~ gentrified_2016_2011 +
## pct_esp_vert_high_2011b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2608 -1.0193 -0.2532 0.6779 7.6584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.793392 0.172767 10.380 < 2e-16 ***
## gentrified_2016_2011TRUE 0.816183 0.136808 5.966 3.90e-09 ***
## pct_esp_vert_high_2011b500 0.056440 0.008682 6.501 1.53e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.635 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.09102, Adjusted R-squared: 0.08838
## F-statistic: 34.4 on 2 and 687 DF, p-value: 5.795e-15
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(y=pct_esp_vert_diff_high_2011.2017b750, x=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_high_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_high_2011.2017b750, na.rm = TRUE)
)
# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_high_2011.2017b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 68.7 68.74 28.66 1.17e-07 ***
## Residuals 688 1649.8 2.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness
# Linear model
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b750 ~ gentrified_2016_2011,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7009 -1.0767 -0.2509 0.6966 6.7491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.74089 0.07053 38.859 < 2e-16 ***
## gentrified_2016_2011TRUE 0.68781 0.12847 5.354 1.17e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.549 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04, Adjusted R-squared: 0.0386
## F-statistic: 28.67 on 1 and 688 DF, p-value: 1.174e-07
# Accounting for UC in 2011
res.lm <- lm(pct_esp_vert_diff_high_2011.2017b750 ~ gentrified_2016_2011 + pct_esp_vert_high_2011b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)
##
## Call:
## lm(formula = pct_esp_vert_diff_high_2011.2017b750 ~ gentrified_2016_2011 +
## pct_esp_vert_high_2011b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1621 -0.9476 -0.2480 0.6476 6.5868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.708692 0.164555 10.384 < 2e-16 ***
## gentrified_2016_2011TRUE 0.792147 0.125249 6.325 4.57e-10 ***
## pct_esp_vert_high_2011b750 0.057624 0.008359 6.894 1.23e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.499 on 687 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1021, Adjusted R-squared: 0.0995
## F-statistic: 39.06 on 2 and 687 DF, p-value: < 2.2e-16
Below we summarize the trends linking SES and BE (looking at association at the 500m buffer scale and INTERACT study area).
| SES metric | BE metric | UC 2011 trend | UI 2011 -> 2016 trend |
|---|---|---|---|
| Pampalon / MAT 2011 | Bike lane | -*** | x (-** when controlling for UC) |
| Pampalon / MAT 2011 | Greenness | -*** | x (-* when controlling for UC) |
| Pampalon / MAT 2011 | Tree canopy | -*** | -*** |
| Visible Minority 2011 | Bike lane | -*** | -*** |
| Visible Minority 2011 | Greenness | -*** | -* (-** when controlling for UC) |
| Visible Minority 2011 | Tree canopy | -*** | -*** |
| Gentrified 2011-2016 | Bike lane | x | +*** |
| Gentrified 2011-2016 | Greenness | -*** | +** (x when controlling for UC) |
| Gentrified 2011-2016 | Tree canopy | -*** | +*** |
x: no significant association
-: negative association (higher SES metric => lower BE metric)
+: positive association (higher SES metric => higher BE metric)
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Controlling for UC in UI models does not change the association trend; even better, non significant association may become (slightly) significant.
A few ref to properly plot interactions: - https://stats.oarc.ucla.edu/r/seminars/interactions-r/#s4d - https://cran.r-project.org/web/packages/emmeans/vignettes/sophisticated.html
Tackling part 2 of objective #1: a multilevel model to test the reduction in socio-economic inequalities in urban conditions \[𝔼(UC_{ij} \mid X_{ij}) = \beta_{0j} + \beta_{1j} ∗ SES + \beta_{2j} ∗ Time + \beta_{3j} ∗ SES ∗ Time + \epsilon_{ij}\]
Data for bike lanes is available for 2006, 2011 and 2016 whereas greenness (and trees) is only available for 2011 and 2016.
# Get SCOREMAT
.bei_df_mlm.1 <- bei_df %>%
select(CT_UID, starts_with("wSCOREMAT")) %>%
pivot_longer(cols = starts_with("wSCOREMAT"),
names_to = "Year", names_prefix = "wSCOREMAT.",
values_to = "wSCOREMAT", values_drop_na = TRUE)
# Get vis_minority
.bei_df_mlm.2 <- bei_df %>%
select(CT_UID, starts_with("vis_minority")) %>%
pivot_longer(cols = starts_with("vis_minority"),
names_to = "Year", names_prefix = "vis_minority_",
values_to = "vis_minority", values_drop_na = TRUE)
# Get gentrified flag
.bei_df_mlm.3 <- bei_df %>%
select(CT_UID, starts_with("gentrified_")) %>%
pivot_longer(cols = starts_with("gentrified_"),
names_to = "Year_span", names_prefix = "gentrified_",
values_to = "gentrified", values_drop_na = TRUE) %>%
extract(Year_span, "Year", "(\\d{4})_*")
# Get bike_lane
.bei_df_mlm.4 <- bei_df %>%
select(CT_UID, starts_with("Bike_lane.by.street")) %>%
pivot_longer(cols = starts_with("Bike_lane.by.street"),
names_to = "Var", names_prefix = "Bike_lane.by.street.",
values_to = "bike_lane.by.street", values_drop_na = TRUE) %>%
extract("Var", c("Year", "Spatial.scale"), "(\\d{4})([[:alnum:]]+)")
# Get Canopy
.bei_df_mlm.5 <- bei_df %>%
select(CT_UID, starts_with("pct_esp_vert_20")) %>%
pivot_longer(cols = starts_with("pct_esp_vert_"),
names_to = "Var", names_prefix = "pct_esp_vert_",
values_to = "pct_esp_vert", values_drop_na = TRUE) %>%
extract("Var", c("Year", "Spatial.scale"), "(\\d{4})([[:alnum:]]+)") %>%
filter(Year %in% c('2011', '2017')) %>%
mutate(Year = case_when(Year == '2017' ~ '2016',
TRUE ~ Year))
# Get Canopy
.bei_df_mlm.6 <- bei_df %>%
select(CT_UID, starts_with("pct_esp_vert_high_20")) %>%
pivot_longer(cols = starts_with("pct_esp_vert_high"),
names_to = "Var", names_prefix = "pct_esp_vert_high_",
values_to = "pct_esp_vert_high", values_drop_na = TRUE) %>%
extract("Var", c("Year", "Spatial.scale"), "(\\d{4})([[:alnum:]]+)") %>%
filter(Year %in% c('2011', '2017')) %>%
mutate(Year = case_when(Year == '2017' ~ '2016',
TRUE ~ Year))
# Combine all subsets
bei_df_mlm <- bei_df %>%
select(CT_UID, interact_aoi, Population) %>%
full_join(.bei_df_mlm.1, by=c("CT_UID")) %>%
full_join(.bei_df_mlm.2, by=c("CT_UID", "Year")) %>%
full_join(.bei_df_mlm.3, by=c("CT_UID", "Year")) %>%
full_join(.bei_df_mlm.4, by=c("CT_UID", "Year")) %>%
full_join(.bei_df_mlm.5, by=c("CT_UID", "Year", "Spatial.scale")) %>%
full_join(.bei_df_mlm.6, by=c("CT_UID", "Year", "Spatial.scale")) %>%
units::drop_units() %>%
#mutate(Year = factor(Year, levels = c("2006", "2011", "2016")))
mutate(Year = as.numeric(Year))
Here, SES is measured through Pampalon Material Score and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area. Using 2006 as reference year.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.1 <- lmer(bike_lane.by.street ~ wSCOREMAT*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
summary(res.mlm.1)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bike_lane.by.street ~ wSCOREMAT * delta.Year + (1 | CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 12079.8 12113.6 -6033.9 12067.8 2066
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6767 -0.5590 0.0050 0.5021 3.9861
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 21.75 4.664
## Residual 10.14 3.185
## Number of obs: 2072, groups: CT_UID, 693
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.12580 0.20903 29.306
## wSCOREMAT -29.66509 4.78235 -6.203
## delta.Year 0.60638 0.01715 35.363
## wSCOREMAT:delta.Year -1.93134 0.44834 -4.308
##
## Correlation of Fixed Effects:
## (Intr) wSCOREMAT dlt.Yr
## wSCOREMAT -0.029
## delta.Year -0.410 -0.002
## wSCOREMAT:. 0.004 -0.506 -0.007
# Plot interactions
plot_model(res.mlm.1, type = 'int')
emmip(res.mlm.1, delta.Year~wSCOREMAT, at = list(delta.Year = c(0, 5, 10), wSCOREMAT=seq(-.2, .2, by=.01)), CI=TRUE)
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
Here, SES is measured through the % of visible minority population and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.2 <- lmer(bike_lane.by.street ~ vis_minority*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
summary(res.mlm.2)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bike_lane.by.street ~ vis_minority * delta.Year + (1 | CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 11934.5 11968.3 -5961.2 11922.5 2059
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6629 -0.5617 -0.0067 0.4941 3.7786
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 19.187 4.380
## Residual 9.933 3.152
## Number of obs: 2065, groups: CT_UID, 691
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 8.270127 0.329009 25.137
## vis_minority -0.106487 0.012148 -8.766
## delta.Year 0.845371 0.032637 25.902
## vis_minority:delta.Year -0.005666 0.001114 -5.086
##
## Correlation of Fixed Effects:
## (Intr) vs_mnr dlt.Yr
## vis_minorty -0.795
## delta.Year -0.340 0.245
## vs_mnrty:.Y 0.437 -0.537 -0.821
# Plot interactions
plot_model(res.mlm.2, type = 'int')
Here, SES is measured through Pampalon Material Score combined with the % of visible minority population and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.3 <- lmer(bike_lane.by.street ~ wSCOREMAT*delta.Year + vis_minority*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
## Warning: Some predictor variables are on very different scales: consider
## rescaling
summary(res.mlm.3)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bike_lane.by.street ~ wSCOREMAT * delta.Year + vis_minority *
## delta.Year + (1 | CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 11920.3 11965.4 -5952.1 11904.3 2057
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6895 -0.5686 0.0009 0.4971 3.8313
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 19.285 4.391
## Residual 9.792 3.129
## Number of obs: 2065, groups: CT_UID, 691
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.877530 0.351323 22.422
## wSCOREMAT -16.282727 5.178330 -3.144
## delta.Year 0.812943 0.036822 22.077
## vis_minority -0.086567 0.013567 -6.381
## wSCOREMAT:delta.Year -0.491004 0.518795 -0.946
## delta.Year:vis_minority -0.005020 0.001297 -3.871
##
## Correlation of Fixed Effects:
## (Intr) wSCOREMAT dlt.Yr vs_mnr wSCOREMAT:
## wSCOREMAT 0.355
## delta.Year -0.324 -0.139
## vis_minorty -0.823 -0.449 0.242
## wSCOREMAT:. -0.174 -0.499 0.463 0.203
## dlt.Yr:vs_m 0.438 0.260 -0.858 -0.514 -0.522
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
# Plot interactions
plot_model(res.mlm.3, type = 'pred', terms = c('wSCOREMAT', 'vis_minority', 'delta.Year'))
Here, SES is measured through Pampalon Material Score combined to % of visible minority population and urban conditions as the % of greenness within 500m buffers around CT for the INTERACT study area.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.4 <- lmer(pct_esp_vert ~ wSCOREMAT*delta.Year + vis_minority*delta.Year + (1 | CT_UID), data = mlm_data)
## Warning: Some predictor variables are on very different scales: consider
## rescaling
summary(res.mlm.4)
## Linear mixed model fit by REML ['lmerMod']
## Formula: pct_esp_vert ~ wSCOREMAT * delta.Year + vis_minority * delta.Year +
## (1 | CT_UID)
## Data: mlm_data
##
## REML criterion at convergence: 8727.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.1003 -0.3850 0.0039 0.3822 8.3635
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 143.15 11.965
## Residual 3.66 1.913
## Number of obs: 1378, groups: CT_UID, 691
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 32.779289 0.693038 47.298
## wSCOREMAT -36.172408 7.773713 -4.653
## delta.Year 0.845882 0.049396 17.125
## vis_minority -0.031168 0.021497 -1.450
## wSCOREMAT:delta.Year 0.003342 0.626849 0.005
## delta.Year:vis_minority -0.002711 0.001556 -1.743
##
## Correlation of Fixed Effects:
## (Intr) wSCOREMAT dlt.Yr vs_mnr wSCOREMAT:
## wSCOREMAT 0.156
## delta.Year -0.297 -0.026
## vis_minorty -0.707 -0.325 0.187
## wSCOREMAT:. -0.162 -0.491 0.477 0.196
## dlt.Yr:vs_m 0.401 0.232 -0.861 -0.482 -0.552
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
# Plot interactions
plot_model(res.mlm.4, type = 'pred', terms = c('wSCOREMAT', 'vis_minority', 'delta.Year'))
Here, SES is measured through Pampalon Material Score combined with % of visible minority population and urban conditions as the % of trees within 500m buffers around CT for the INTERACT study area.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.5 <- lmer(pct_esp_vert_high ~ wSCOREMAT*delta.Year + vis_minority*delta.Year + (1 | CT_UID), data = mlm_data)
## Warning: Some predictor variables are on very different scales: consider
## rescaling
summary(res.mlm.5)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## pct_esp_vert_high ~ wSCOREMAT * delta.Year + vis_minority * delta.Year +
## (1 | CT_UID)
## Data: mlm_data
##
## REML criterion at convergence: 7254.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.98339 -0.41949 0.00332 0.40454 3.06727
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 46.920 6.850
## Residual 1.302 1.141
## Number of obs: 1378, groups: CT_UID, 691
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.540e+01 4.042e-01 38.100
## wSCOREMAT -1.923e+01 4.604e+00 -4.177
## delta.Year 6.103e-01 2.942e-02 20.744
## vis_minority -3.527e-02 1.269e-02 -2.779
## wSCOREMAT:delta.Year -2.492e+00 3.738e-01 -6.667
## delta.Year:vis_minority -5.059e-04 9.277e-04 -0.545
##
## Correlation of Fixed Effects:
## (Intr) wSCOREMAT dlt.Yr vs_mnr wSCOREMAT:
## wSCOREMAT 0.162
## delta.Year -0.308 -0.030
## vis_minorty -0.716 -0.331 0.195
## wSCOREMAT:. -0.167 -0.496 0.477 0.200
## dlt.Yr:vs_m 0.411 0.235 -0.862 -0.487 -0.551
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
# Plot interactions
plot_model(res.mlm.5, type = 'pred', terms = c('wSCOREMAT', 'vis_minority', 'delta.Year'))
Here, SES is measured through Pampalon Material Score and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area. Modeled as random slope, not just random intercept.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.6 <- lmer(bike_lane.by.street ~ wSCOREMAT*delta.Year + (delta.Year | CT_UID), data = mlm_data, REML = FALSE)
summary(res.mlm.6)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bike_lane.by.street ~ wSCOREMAT * delta.Year + (delta.Year |
## CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 11900.1 11945.2 -5942.1 11884.1 2064
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00916 -0.40756 0.00695 0.36142 2.84899
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## CT_UID (Intercept) 20.2983 4.5054
## delta.Year 0.1717 0.4144 -0.08
## Residual 5.8716 2.4231
## Number of obs: 2072, groups: CT_UID, 693
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.12408 0.19092 32.076
## wSCOREMAT -24.50034 4.37362 -5.602
## delta.Year 0.60615 0.02046 29.621
## wSCOREMAT:delta.Year -2.18277 0.51916 -4.204
##
## Correlation of Fixed Effects:
## (Intr) wSCOREMAT dlt.Yr
## wSCOREMAT -0.029
## delta.Year -0.272 -0.001
## wSCOREMAT:. -0.004 -0.418 -0.007
# Check if model with random slope significantly improves over random intercept (see http://www.bodowinter.com/tutorial/bw_LME_tutorial1.pdf)
res.mlm.6.null <- lmer(bike_lane.by.street ~ wSCOREMAT*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
anova(res.mlm.6.null, res.mlm.6)
Here, SES is measured through Pampalon Material and % of visible minority and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area. Here, the three-way interaction is modeled, contrary to model 3, where the same variables are considered through two-way interaction (Year & SCOREMAT and Year & vis_minority).
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.7 <- lmer(bike_lane.by.street ~ wSCOREMAT*vis_minority*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
## Warning: Some predictor variables are on very different scales: consider
## rescaling
summary(res.mlm.7)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bike_lane.by.street ~ wSCOREMAT * vis_minority * delta.Year +
## (1 | CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 11923.4 11979.8 -5951.7 11903.4 2055
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7095 -0.5622 0.0039 0.4893 3.8351
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 19.305 4.394
## Residual 9.782 3.128
## Number of obs: 2065, groups: CT_UID, 691
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.921273 0.356795 22.201
## wSCOREMAT -20.930417 7.600014 -2.754
## vis_minority -0.091188 0.014800 -6.161
## delta.Year 0.811819 0.037344 21.739
## wSCOREMAT:vis_minority 0.202550 0.241270 0.840
## wSCOREMAT:delta.Year -0.519784 0.818174 -0.635
## vis_minority:delta.Year -0.004881 0.001427 -3.421
## wSCOREMAT:vis_minority:delta.Year -0.003483 0.024586 -0.142
##
## Correlation of Fixed Effects:
## (Intr) wSCOREMAT vs_mnr dlt.Yr wSCOREMAT:v_ wSCOREMAT:. vs_:.Y
## wSCOREMAT 0.115
## vis_minorty -0.811 0.010
## delta.Year -0.337 -0.019 0.264
## wSCOREMAT:v_ 0.168 -0.732 -0.396 -0.099
## wSCOREMAT:. -0.026 -0.435 -0.034 0.162 0.288
## vs_mnrty:.Y 0.451 -0.037 -0.550 -0.839 0.265 0.008
## wSCOREMAT:_: -0.132 0.414 0.263 0.169 -0.552 -0.758 -0.416
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
# Plot interactions
plot_model(res.mlm.7, type = 'pred', terms = c('wSCOREMAT', 'vis_minority', 'delta.Year'))
# Check if model with three-way interaction significantly improves over two-way interactions (model 3)
anova(res.mlm.3, res.mlm.7)
Here, SES is measured through gentrification status and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area. Using 2006 as reference year.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.8 <- lmer(bike_lane.by.street ~ factor(gentrified)*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
summary(res.mlm.8)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: bike_lane.by.street ~ factor(gentrified) * delta.Year + (1 |
## CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 11673.1 11706.7 -5830.6 11661.1 1991
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8787 -0.5363 -0.0127 0.4797 3.9410
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 23.081 4.804
## Residual 9.962 3.156
## Number of obs: 1997, groups: CT_UID, 691
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.74580 0.23623 28.556
## factor(gentrified)TRUE -2.40404 0.29387 -8.181
## delta.Year 0.47163 0.02195 21.485
## factor(gentrified)TRUE:delta.Year 0.44069 0.04161 10.590
##
## Correlation of Fixed Effects:
## (Intr) fc()TRUE dlt.Yr
## fctr(g)TRUE -0.399
## delta.Year -0.487 0.432
## fc()TRUE:.Y 0.291 -0.710 -0.600
# Plot interactions
plot_model(res.mlm.8, type = 'pred', terms = c('gentrified', 'delta.Year'))
Here, SES is measured through gentrification status and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area. Using 2006 as reference year.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.9 <- lmer(pct_esp_vert ~ factor(gentrified)*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
summary(res.mlm.9)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: pct_esp_vert ~ factor(gentrified) * delta.Year + (1 | CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 8691.4 8722.7 -4339.7 8679.4 1357
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.2575 -0.3735 -0.0241 0.3650 8.5444
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 149.043 12.208
## Residual 3.732 1.932
## Number of obs: 1363, groups: CT_UID, 689
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 32.44059 0.51570 62.906
## factor(gentrified)TRUE -1.89689 0.46658 -4.066
## delta.Year 0.71055 0.02757 25.769
## factor(gentrified)TRUE:delta.Year 0.21239 0.05747 3.696
##
## Correlation of Fixed Effects:
## (Intr) fc()TRUE dlt.Yr
## fctr(g)TRUE -0.285
## delta.Year -0.405 0.600
## fc()TRUE:.Y 0.261 -0.920 -0.646
# Plot interactions
plot_model(res.mlm.9, type = 'pred', terms = c('gentrified', 'delta.Year'))
Here, SES is measured through gentrification status and urban conditions as the ratio of bike lanes to streets within 500m buffers around CT for the INTERACT study area. Using 2006 as reference year.
# Define multilevel model | buf 500m / INTERACT study area
mlm_data <- bei_df_mlm %>%
filter(Spatial.scale == 'b500' & interact_aoi) %>%
mutate(delta.Year = Year - 2006)
res.mlm.10 <- lmer(pct_esp_vert_high ~ factor(gentrified)*delta.Year + (1 | CT_UID), data = mlm_data, REML = FALSE)
summary(res.mlm.10)
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: pct_esp_vert_high ~ factor(gentrified) * delta.Year + (1 | CT_UID)
## Data: mlm_data
##
## AIC BIC logLik deviance df.resid
## 7330.6 7361.9 -3659.3 7318.6 1357
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.97493 -0.40607 -0.00555 0.40257 3.14651
##
## Random effects:
## Groups Name Variance Std.Dev.
## CT_UID (Intercept) 54.531 7.385
## Residual 1.385 1.177
## Number of obs: 1363, groups: CT_UID, 689
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 14.96985 0.31234 47.927
## factor(gentrified)TRUE -2.08819 0.28420 -7.348
## delta.Year 0.52563 0.01680 31.294
## factor(gentrified)TRUE:delta.Year 0.25392 0.03501 7.253
##
## Correlation of Fixed Effects:
## (Intr) fc()TRUE dlt.Yr
## fctr(g)TRUE -0.287
## delta.Year -0.408 0.600
## fc()TRUE:.Y 0.262 -0.920 -0.646
# Plot interactions
plot_model(res.mlm.10, type = 'pred', terms = c('gentrified', 'delta.Year'))